Forming Crowds And Providing Access To Crowd Data In A Mobile Environment

ABSTRACT

A system and method are provided for forming crowds of users and providing access to corresponding crowd data. In one embodiment, a central system, which includes one or more servers, operates to obtain current locations for users of mobile devices. The system forms a crowd including a number of users based on the current locations of the number of users using a spatial crowd formation process based on an optimal inclusion distance that is a function of density of users of the plurality of users within a bounding region. The central system then generates crowd data for the crowd and provides access to the crowd data for the crowd. In one embodiment, the crowd data for the crowd includes an aggregate profile for the crowd. In another embodiment, the crowd data includes data characterizing the crowd. The central system provides access to the crowd data by serving crowd data requests.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.12/654,532, which was filed Dec. 23, 2009, which claims the benefit ofprovisional patent application Ser. No. 61/149,205, filed Feb. 2, 2009,provisional patent application Ser. No. 61/227,192, filed Jul. 21, 2009,and provisional patent application Ser. No. 61/236,296, filed Aug. 24,2009, the disclosures of which are hereby incorporated by reference intheir entireties.

RELATED APPLICATIONS

This application is related to:

-   -   U.S. patent application Ser. No. 12/645,535, entitled        MAINTAINING A HISTORICAL RECORD OF ANONYMIZED USER PROFILE DATA        BY LOCATION FOR USERS IN A MOBILE ENVIRONMENT, which was filed        Dec. 23, 2009;    -   U.S. patent application Ser. No. 12/645,539, entitled ANONYMOUS        CROWD TRACKING, which was filed Dec. 23, 2009;    -   U.S. patent application Ser. No. 12/645,544, entitled MODIFYING        A USER'S CONTRIBUTION TO AN AGGREGATE PROFILE BASED ON TIME        BETWEEN LOCATION UPDATES AND EXTERNAL EVENTS, which was filed        Dec. 23, 2009;    -   U.S. patent application Ser. No. 12/645,546, entitled CROWD        FORMATION FOR MOBILE DEVICE USERS, which was filed Dec. 23,        2009;    -   U.S. patent application Ser. No. 12/645,556, entitled SERVING A        REQUEST FOR DATA FROM A HISTORICAL RECORD OF ANONYMIZED USER        PROFILE DATA IN A MOBILE ENVIRONMENT, which was filed Dec. 23,        2009; and    -   U.S. patent application Ser. No. 12/645,560, entitled HANDLING        CROWD REQUESTS FOR LARGE GEOGRAPHIC AREAS, which was filed Dec.        23, 2009;        all of which are commonly owned and assigned and are hereby        incorporated herein by reference in their entireties.

FIELD OF THE DISCLOSURE

The present disclosure relates to forming crowds of users in a mobileenvironment and providing access to crowd data for those crowds.

BACKGROUND

With the growing popularity of mobile smart phones, such as the Apple®iPhone, mobile social networking applications are becoming extremelypopular. However, a major concern with current mobile social networkingapplications is user privacy. What is needed is a mobile socialnetworking application that operates within a strict privacy framework.

SUMMARY

The present disclosure provides a system and method for forming crowdsof users and providing access to corresponding crowd data. In oneembodiment, a central system, which includes one or more servers,operates to obtain current locations for users of mobile devices. Thecentral system forms a crowd including a number of users based on thecurrent locations of the number of users using a spatial crowd formationprocess based on an optimal inclusion distance that is a function ofdensity of users of the plurality of users within a bounding region forthe spatial crowd formation process. The optimal inclusion distance issubject to change as the crowd is formed. The central system thengenerates crowd data for the crowd and provides access to the crowd datafor the crowd in response to receiving a request for the crowd data. Inone embodiment, the crowd data for the crowd includes an aggregateprofile for the crowd generated based on user profiles of the users inthe crowd and, in some embodiments, a user profile or select subset ofthe user profile of a requestor or a target user profile. In anotherembodiment, the crowd data includes data characterizing the crowd suchas, but not limited to, data identifying a degree of fragmentation ofthe crowd, a best-case average degree of separation (DOS) for one ormore crowd fragments in the crowd, a worst-case average DOS for one ormore crowd fragments in the crowd, a best-case average DOS for thecrowd, a worst-case average DOS for the crowd, a degree ofbidirectionality of friend relationships in one or more crowd fragmentsin the crowd, and/or a degree of bidirectionality of friendrelationships in the crowd.

Those skilled in the art will appreciate the scope of the presentinvention and realize additional aspects thereof after reading thefollowing detailed description in association with the accompanyingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings incorporated in and forming a part of thisspecification illustrate several aspects of the invention, and togetherwith the description serve to explain the principles of the invention.

FIG. 1 illustrates a Mobile Aggregate Profile (MAP) system according toone embodiment of the present disclosure;

FIG. 2 is a block diagram of the MAP server of FIG. 1 according to oneembodiment of the present disclosure;

FIG. 3 is a block diagram of the MAP client of one of the mobile devicesof FIG. 1 according to one embodiment of the present disclosure;

FIG. 4 illustrates the operation of the system of FIG. 1 to provide userprofiles and current locations of the users of the mobile devices to theMAP server according to one embodiment of the present disclosure;

FIG. 5 illustrates the operation of the system of FIG. 1 to provide userprofiles and current locations of the users of the mobile devices to theMAP server according to another embodiment of the present disclosure;

FIGS. 6 and 7 graphically illustrate bucketization of users according tolocation for purposes of maintaining a historical record of anonymizeduser profile data by location according to one embodiment of the presentdisclosure;

FIG. 8 is a flow chart illustrating the operation of a foregroundbucketization process performed by the MAP server to maintain the listsof users for location buckets for purposes of maintaining a historicalrecord of anonymized user profile data by location according to oneembodiment of the present disclosure;

FIG. 9 is a flow chart illustrating the anonymization and storageprocess performed by the MAP server for the location buckets in order tomaintain a historical record of anonymized user profile data by locationaccording to one embodiment of the present disclosure;

FIG. 10 graphically illustrates anonymization of a user record accordingto one embodiment of the present disclosure;

FIG. 11 is a flow chart for a quadtree based storage process that may beused to store anonymized user profile data for location bucketsaccording to one embodiment of the present disclosure;

FIG. 12 is a flow chart illustrating a quadtree algorithm that may beused to process the location buckets for storage of the anonymized userprofile data according to one embodiment of the present disclosure;

FIGS. 13A through 13E graphically illustrate the process of FIG. 12 forthe generation of a quadtree data structure for one exemplary basequadtree region;

FIG. 14 illustrates the operation of the system of FIG. 1 wherein amobile device is enabled to request and receive historical data from theMAP server according to one embodiment of the present disclosure;

FIGS. 15A and 15B illustrate a flow chart for a process for generatinghistorical data in a time context in response to a historical requestfrom a mobile device according to one embodiment of the presentdisclosure;

FIG. 16 is an exemplary Graphical User Interface (GUI) that may beprovided by the MAP application of one of the mobile devices of FIG. 1in order to present historical aggregate profile data in a time contextaccording to one embodiment of the present disclosure;

FIGS. 17A and 17B illustrate a flow chart for a process for generatinghistorical data in a geographic context in response to a historicalrequest from a mobile device according to one embodiment of the presentdisclosure;

FIG. 18 illustrates an exemplary GUI that may be provided by the MAPapplication of one of the mobile devices of FIG. 1 to present historicaldata in the geographic context according to one embodiment of thepresent disclosure;

FIG. 19 illustrates the operation of the system of FIG. 1 wherein thesubscriber device is enabled to request and receive historical data fromthe MAP server according to one embodiment of the present disclosure;

FIGS. 20A and 20B illustrate a process for generating historical data ina time context in response to a historical request from a subscriberdevice according to one embodiment of the present disclosure;

FIGS. 21A and 21B illustrate a process for generating historical data ina geographic context in response to a historical request from asubscriber device according to one embodiment of the present disclosure.

FIG. 22 is a flow chart for a spatial crowd formation process accordingto one embodiment of the present disclosure;

FIGS. 23A through 23D graphically illustrate the crowd formation processof FIG. 22 for an exemplary bounding box;

FIGS. 24A through 24D illustrate a flow chart for a spatial crowdformation process according to another embodiment of the presentdisclosure;

FIGS. 25A through 25D graphically illustrate the crowd formation processof FIGS. 24A through 24D for a scenario where the crowd formationprocess is triggered by a location update for a user having no oldlocation;

FIGS. 26A through 26F graphically illustrate the crowd formation processof FIGS. 24A through 24D for a scenario where the new and old boundingboxes overlap;

FIGS. 27A through 27E graphically illustrate the crowd formation processof FIGS. 24A through 24D in a scenario where the new and old boundingboxes do not overlap;

FIG. 28 illustrates the operation the system of FIG. 1 to enable themobile devices to request crowd data for currently formed crowdsaccording to one embodiment of the present disclosure;

FIG. 29A is a flow chart for a process for generating aggregate profilesfor crowds identified in response to a crowd request from a mobiledevice according to one embodiment of the present disclosure;

FIG. 29B is a flow chart for a process for generating aggregate profilesfor crowds identified in response to a crowd request from a mobiledevice according to another embodiment of the present disclosure;

FIG. 30 illustrates the operation of the system of FIG. 1 to enable asubscriber device to request crowd data for current crowds according toone embodiment of the present disclosure;

FIG. 31 is a flow chart for a process for generating aggregate profilesfor crowds identified for a crowd request in response to a crowd requestfrom a subscriber device according to one embodiment of the presentdisclosure;

FIGS. 32A through 32E illustrate a GUI for an exemplary embodiment ofthe MAP application of one of the mobile devices of FIG. 1 according toone embodiment of the present disclosure;

FIGS. 33A through 33C illustrate an exemplary web interface provided bythe MAP server and presented to the subscriber at the subscriber deviceaccording to one embodiment of the present disclosure;

FIG. 34 is a flow chart illustrating a spatial crowd fragmentationprocess according to one embodiment of the present disclosure;

FIGS. 35A and 35B graphically illustrate the spatial crowd fragmentationprocess of FIG. 34 for an exemplary crowd;

FIG. 36 illustrates a connectivity-based crowd fragmentation processaccording to one embodiment of the present disclosure;

FIGS. 37A and 37B graphically illustrate the connectivity-based crowdfragmentation process of FIG. 36 for an exemplary crowd;

FIG. 38 is a flow chart illustrating a recursive crowd fragmentationthat uses both spatial crowd formation and connectivity-based crowdformation according to one embodiment of the present disclosure;

FIG. 39 is a flow chart illustrating a recursive crowd fragmentationthat uses both spatial crowd formation and connectivity-based crowdformation according to another embodiment of the present disclosure;

FIGS. 40A and 40B illustrate an exemplary graphical representation ofthe degree of fragmentation for a crowd according to one embodiment ofthe present disclosure;

FIG. 41 is a flow chart for a process for determining a best-case andworst-case average degree of separation (DOS) for a crowd fragment of acrowd according to one embodiment of the present disclosure;

FIG. 42 is a more detailed flow chart illustrating the process fordetermining a best-case and worst-case average DOS for a crowd fragmentaccording to one embodiment of the present disclosure;

FIGS. 43A through 43D illustrate an exemplary graphical representationof the best-case and worst-case average DOS for a crowd fragmentaccording to one embodiment of the present disclosure;

FIG. 44 is a flow chart for a process of determining a degree ofbidirectionality of relationships between users in a crowd fragmentaccording to one embodiment of the present disclosure;

FIGS. 45A through 45C illustrate an exemplary graphical representationof the degree of bidirectionality of friendship relationships for acrowd fragment according to one embodiment of the present disclosure;

FIG. 46 is a flow chart for a process for generating a quality level foran aggregate profile for a crowd according to one embodiment of thepresent disclosure;

FIG. 47 illustrates an exemplary GUI for presenting an aggregate profilefor a crowd and a quality level of the aggregate profile generated usingthe process of FIG. 46 according to one embodiment of the presentdisclosure;

FIG. 48 illustrates another exemplary GUI for presenting an aggregateprofile for a crowd and a quality level of the aggregate profilegenerated using the process of FIG. 46 according to another embodimentof the present disclosure;

FIG. 49 illustrates a flow chart for a process for generating confidencefactors for keywords included in an aggregate profile for a crowd basedon confidence levels for current locations of users in the crowdaccording to one embodiment of the present disclosure;

FIG. 50 illustrates an exemplary GUI for presenting an aggregate profilefor a crowd including an indication of a confidence level for each of anumber of keywords in the aggregate profile according to one embodimentof the present disclosure;

FIG. 51 graphically illustrates modification of the confidence level ofthe current location of a user according to one embodiment of thepresent disclosure;

FIG. 52 illustrates the operation of the system of FIG. 1 to perform aprocess for efficiently handling requests for crowd data for largegeographic areas according to one embodiment of the present disclosure;

FIGS. 53A through 53E illustrate an exemplary series of outwardlyradiating, concentric geographic regions for a number of hotspotsidentified for a bounding region established by the MAP server inresponse to a request for crowd data according to one embodiment of thepresent disclosure;

FIG. 54 graphically illustrates one exemplary variation to the follow-uprequest regions illustrated in FIGS. 53A through 53E;

FIG. 55 illustrates exemplary data records that may be used to representcrowds, users, crowd snapshots, and anonymous users according to oneembodiment of the present disclosure;

FIGS. 56A through 56D illustrate one embodiment of a spatial crowdformation process that may be used to enable crowd tracking according toone embodiment of the present disclosure;

FIG. 57 illustrates a process for creating crowd snapshots according toone embodiment of the present disclosure;

FIG. 58 illustrates a process that may be used to re-establish crowdsand detect crowd splits according to one embodiment of the presentdisclosure;

FIG. 59 graphically illustrates the process of re-establishing a crowdfor an exemplary crowd according to one embodiment of the presentdisclosure;

FIG. 60 graphically illustrates the process for capturing a crowd splitfor an exemplary crowd according to one embodiment of the presentdisclosure;

FIG. 61 graphically illustrates the merging of two exemplarypre-existing crowds according to one embodiment of the presentdisclosure;

FIG. 62 illustrates the operation of the MAP server of FIG. 1 to serve arequest for crowd tracking data for a crowd according to one embodimentof the present disclosure;

FIG. 63 illustrates the operation of the MAP server of FIG. 1 to enablealerts according to one embodiment of the present disclosure;

FIG. 64 is a block diagram of the MAP server of FIG. 1 according to oneembodiment of the present disclosure;

FIG. 65 is a block diagram of one of the mobile devices of FIG. 1according to one embodiment of the present disclosure;

FIG. 66 is a block diagram of the subscriber device of FIG. 1 accordingto one embodiment of the present disclosure; and

FIG. 67 is a block diagram of a computing device operating to host thethird-party service of FIG. 1 according to one embodiment of the presentdisclosure.

DETAILED DESCRIPTION

The embodiments set forth below represent the necessary information toenable those skilled in the art to practice the invention and illustratethe best mode of practicing the invention. Upon reading the followingdescription in light of the accompanying drawings, those skilled in theart will understand the concepts of the invention and will recognizeapplications of these concepts not particularly addressed herein. Itshould be understood that these concepts and applications fall withinthe scope of the disclosure and the accompanying claims.

FIG. 1 illustrates a Mobile Aggregate Profile (MAP) system 10 accordingto one embodiment of the present disclosure. In this embodiment, thesystem 10 includes a MAP server 12, one or more profile servers 14, alocation server 16, a number of mobile devices 18-1 through 18-N havingassociated users 20-1 through 20-N, a subscriber device 22 having anassociated subscriber 24, and a third-party service 26 communicativelycoupled via a network 28. The network 28 may be any type of network orany combination of networks. Specifically, the network 28 may includewired components, wireless components, or both wired and wirelesscomponents. In one exemplary embodiment, the network 28 is a distributedpublic network such as the Internet, where the mobile devices 18-1through 18-N are enabled to connect to the network 28 via local wirelessconnections (e.g., WiFi or IEEE 802.11 connections) or wirelesstelecommunications connections (e.g., 3G or 4G telecommunicationsconnections such as GSM, LTE, W-CDMA, or WiMAX connections).

As discussed below in detail, the MAP server 12 operates to obtaincurrent locations, including location updates, and user profiles of theusers 20-1 through 20-N of the mobile devices 18-1 through 18-N. Thecurrent locations of the users 20-1 through 20-N can be expressed aspositional geographic coordinates such as latitude-longitude pairs, anda height vector (if applicable), or any other similar informationcapable of identifying a given physical point in space in atwo-dimensional or three-dimensional coordinate system. Using thecurrent locations and user profiles of the users 20-1 through 20-N, theMAP server 12 is enabled to provide a number of features such as, butnot limited to, maintaining a historical record of anonymized userprofile data by location, generating aggregate profile data over timefor a Point of Interest (POI) or Area of Interest (AOI) using thehistorical record of anonymized user profile data, identifying crowds ofusers using current locations and/or user profiles of the users 20-1through 20-N, generating aggregate profiles for crowds of users at a POIor in an AOI using the current user profiles of users in the crowds, andcrowd tracking. Note that while the MAP server 12 is illustrated as asingle server for simplicity and ease of discussion, it should beappreciated that the MAP server 12 may be implemented as a singlephysical server or multiple physical servers operating in acollaborative manner for purposes of redundancy and/or load sharing.

In general, the one or more profile servers 14 operate to store userprofiles for a number of persons including the users 20-1 through 20-Nof the mobile devices 18-1 through 18-N. For example, the one or moreprofile servers 14 may be servers providing social network services suchthe Facebook® social networking service, the MySpace® social networkingservice, the LinkedIN® social networking service, and/or the like. Asdiscussed below, using the one or more profile servers 14, the MAPserver 12 is enabled to directly or indirectly obtain the user profilesof the users 20-1 through 20-N of the mobile devices 18-1 through 18-N.The location server 16 generally operates to receive location updatesfrom the mobile devices 18-1 through 18-N and make the location updatesavailable to entities such as, for instance, the MAP server 12. In oneexemplary embodiment, the location server 16 is a server operating toprovide Yahoo!'s FireEagle service.

The mobile devices 18-1 through 18-N may be mobile smart phones,portable media player devices, mobile gaming devices, or the like. Someexemplary mobile devices that may be programmed or otherwise configuredto operate as the mobile devices 18-1 through 18-N are the Apple®iPhone, the Palm Pre, the Samsung Rogue, the Blackberry Storm, and theApple® iPod Touch® device. However, this list of exemplary mobiledevices is not exhaustive and is not intended to limit the scope of thepresent disclosure.

The mobile devices 18-1 through 18-N include MAP clients 30-1 through30-N, MAP applications 32-1 through 32-N, third-party applications 34-1through 34-N, and location functions 36-1 through 36-N, respectively.Using the mobile device 18-1 as an example, the MAP client 30-1 ispreferably implemented in software. In general, in the preferredembodiment, the MAP client 30-1 is a middleware layer operating tointerface an application layer (i.e., the MAP application 32-1 and thethird-party applications 34-1) to the MAP server 12. More specifically,the MAP client 30-1 enables the MAP application 32-1 and the third-partyapplications 34-1 to request and receive data from the MAP server 12. Inaddition, the MAP client 30-1 enables applications, such as the MAPapplication 32-1 and the third-party applications 34-1, to access datafrom the MAP server 12. For example, as discussed below in detail, theMAP client 30-1 enables the MAP application 32-1 to request anonymizedaggregate profiles for crowds of users located at a POI or within an AOIand/or request anonymized historical user profile data for a POI or AOI.

The MAP application 32-1 is also preferably implemented in software. TheMAP application 32-1 generally provides a user interface componentbetween the user 20-1 and the MAP server 12. More specifically, amongother things, the MAP application 32-1 enables the user 20-1 to initiatehistorical requests for historical data or crowd requests for crowd data(e.g., aggregate profile data and/or crowd characteristics data) fromthe MAP server 12 for a POI or AOI. The MAP application 32-1 alsoenables the user 20-1 to configure various settings. For example, theMAP application 32-1 may enable the user 20-1 to select a desired socialnetworking service (e.g., Facebook, MySpace, LinkedIN, etc.) from whichto obtain the user profile of the user 20-1 and provide any necessarycredentials (e.g., username and password) needed to access the userprofile from the social networking service.

The third-party applications 34-1 are preferably implemented insoftware. The third-party applications 34-1 operate to access the MAPserver 12 via the MAP client 30-1. The third-party applications 34-1 mayutilize data obtained from the MAP server 12 in any desired manner. Asan example, one of the third party applications 34-1 may be a gamingapplication that utilizes historical aggregate profile data to notifythe user 20-1 of POIs or AOIs where persons having an interest in thegame have historically congregated.

The location function 36-1 may be implemented in hardware, software, ora combination thereof. In general, the location function 36-1 operatesto determine or otherwise obtain the location of the mobile device 18-1.For example, the location function 36-1 may be or include a GlobalPositioning System (GPS) receiver.

The subscriber device 22 is a physical device such as a personalcomputer, a mobile computer (e.g., a notebook computer, a netbookcomputer, a tablet computer, etc.), a mobile smart phone, or the like.The subscriber 24 associated with the subscriber device 22 is a personor entity. In general, the subscriber device 22 enables the subscriber24 to access the MAP server 12 via a web browser 38 to obtain varioustypes of data, preferably for a fee. For example, the subscriber 24 maypay a fee to have access to historical aggregate profile data for one ormore POIs and/or one or more AOIs, pay a fee to have access to crowddata such as aggregate profiles for crowds located at one or more POIsand/or located in one or more AOIs, pay a fee to track crowds, or thelike. Note that the web browser 38 is exemplary. In another embodiment,the subscriber device 22 is enabled to access the MAP server 12 via acustom application.

Lastly, the third-party service 26 is a service that has access to datafrom the MAP server 12 such as a historical aggregate profile data forone or more POIs or one or more AOIs, crowd data such as aggregateprofiles for one or more crowds at one or more POIs or within one ormore AOIs, or crowd tracking data. Based on the data from the MAP server12, the third-party service 26 operates to provide a service such as,for example, targeted advertising. For example, the third-party service26 may obtain anonymous aggregate profile data for one or more crowdslocated at a POI and then provide targeted advertising to known userslocated at the POI based on the anonymous aggregate profile data. Notethat while targeted advertising is mentioned as an exemplary third-partyservice 26, other types of third-party services 26 may additionally oralternatively be provided. Other types of third-party services 26 thatmay be provided will be apparent to one of ordinary skill in the artupon reading this disclosure.

Before proceeding, it should be noted that while the system 10 of FIG. 1illustrates an embodiment where the one or more profile servers 14 andthe location server 16 are separate from the MAP server 12, the presentdisclosure is not limited thereto. In an alternative embodiment, thefunctionality of the one or more profile servers 14 and/or the locationserver 16 may be implemented within the MAP server 12.

FIG. 2 is a block diagram of the MAP server 12 of FIG. 1 according toone embodiment of the present disclosure. As illustrated, the MAP server12 includes an application layer 40, a business logic layer 42, and apersistence layer 44. The application layer 40 includes a user webapplication 46, a mobile client/server protocol component 48, and one ormore data Application Programming Interfaces (APIs) 50. The user webapplication 46 is preferably implemented in software and operates toprovide a web interface for users, such as the subscriber 24, to accessthe MAP server 12 via a web browser. The mobile client/server protocolcomponent 48 is preferably implemented in software and operates toprovide an interface between the MAP server 12 and the MAP clients 30-1through 30-N hosted by the mobile devices 18-1 through 18-N. The dataAPIs 50 enable third-party services, such as the third-party service 26,to access the MAP server 12.

The business logic layer 42 includes a profile manager 52, a locationmanager 54, a history manager 56, a crowd analyzer 58, and anaggregation engine 60, each of which is preferably implemented insoftware. The profile manager 52 generally operates to obtain the userprofiles of the users 20-1 through 20-N directly or indirectly from theone or more profile servers 14 and store the user profiles in thepersistence layer 44. The location manager 54 operates to obtain thecurrent locations of the users 20-1 through 20-N including locationupdates. As discussed below, the current locations of the users 20-1through 20-N may be obtained directly from the mobile devices 18-1through 18-N and/or obtained from the location server 16.

The history manager 56 generally operates to maintain a historicalrecord of anonymized user profile data by location. The crowd analyzer58 operates to form crowds of users. In one embodiment, the crowdanalyzer 58 utilizes a spatial crowd formation algorithm. However, thepresent disclosure is not limited thereto. In addition, the crowdanalyzer 58 may further characterize crowds to reflect degree offragmentation, best-case and worst-case degree of separation (DOS),and/or degree of bi-directionality, as discussed below in more detail.Still further, the crowd analyzer 58 may also operate to track crowds.The aggregation engine 60 generally operates to provide aggregateprofile data in response to requests from the mobile devices 18-1through 18-N, the subscriber device 22, and the third-party service 26.The aggregate profile data may be historical aggregate profile data forone or more POIs or one or more AOIs or aggregate profile data forcrowd(s) currently at one or more POIs or within one or more AOIs.

The persistence layer 44 includes an object mapping layer 62 and adatastore 64. The object mapping layer 62 is preferably implemented insoftware. The datastore 64 is preferably a relational database, which isimplemented in a combination of hardware (i.e., physical data storagehardware) and software (i.e., relational database software). In thisembodiment, the business logic layer 42 is implemented in anobject-oriented programming language such as, for example, Java. Assuch, the object mapping layer 62 operates to map objects used in thebusiness logic layer 42 to relational database entities stored in thedatastore 64. Note that, in one embodiment, data is stored in thedatastore 64 in a Resource Description Framework (RDF) compatibleformat.

In an alternative embodiment, rather than being a relational database,the datastore 64 may be implemented as an RDF datastore. Morespecifically, the RDF datastore may be compatible with RDF technologyadopted by Semantic Web activities. Namely, the RDF datastore may usethe Friend-Of-A-Friend (FOAF) vocabulary for describing people, theirsocial networks, and their interests. In this embodiment, the MAP server12 may be designed to accept raw FOAF files describing persons, theirfriends, and their interests. These FOAF files are currently output bysome social networking services such as Livejournal and Facebook. TheMAP server 12 may then persist RDF descriptions of the users 20-1through 20-N as a proprietary extension of the FOAF vocabulary thatincludes additional properties desired for the MAP system 10.

FIG. 3 illustrates the MAP client 30-1 of FIG. 1 in more detailaccording to one embodiment of the present disclosure. This discussionis equally applicable to the other MAP clients 30-2 through 30-N. Asillustrated, in this embodiment, the MAP client 30-1 includes a MAPaccess API 66, a MAP middleware component 68, and a mobile client/serverprotocol component 70. The MAP access API 66 is implemented in softwareand provides an interface by which the MAP client 30-1 and thethird-party applications 34-1 are enabled to access the MAP server 12.The MAP middleware component 68 is implemented in software and performsthe operations needed for the MAP client 30-1 to operate as an interfacebetween the MAP application 32-1 and the third-party applications 34-1at the mobile device 18-1 and the MAP server 12. The mobileclient/server protocol component 70 enables communication between theMAP client 30-1 and the MAP server 12 via a defined protocol.

FIG. 4 illustrates the operation of the system 10 of FIG. 1 to providethe user profile of the user 20-1 of the mobile device 18-1 to the MAPserver 12 according to one embodiment of the present disclosure. Thisdiscussion is equally applicable to user profiles of the other users20-2 through 20-N of the other mobile devices 18-2 through 18-N. First,an authentication process is performed (step 1000). For authentication,in this embodiment, the mobile device 18-1 authenticates with theprofile server 14 (step 1000A) and the MAP server 12 (step 1000B). Inaddition, the MAP server 12 authenticates with the profile server 14(step 1000C). Preferably, authentication is performed using OpenID orsimilar technology. However, authentication may alternatively beperformed using separate credentials (e.g., username and password) ofthe user 20-1 for access to the MAP server 12 and the profile server 14.Assuming that authentication is successful, the profile server 14returns an authentication succeeded message to the MAP server 12 (step1000D), and the profile server 14 returns an authentication succeededmessage to the MAP client 30-1 of the mobile device 18-1 (step 1000E).

At some point after authentication is complete, a user profile processis performed such that a user profile of the user 20-1 is obtained fromthe profile server 14 and delivered to the MAP server 12 (step 1002). Inthis embodiment, the MAP client 30-1 of the mobile device 18-1 sends aprofile request to the profile server 14 (step 1002A). In response, theprofile server 14 returns the user profile of the user 20-1 to themobile device 18-1 (step 1002B). The MAP client 30-1 of the mobiledevice 18-1 then sends the user profile of the user 20-1 to the MAPserver 12 (step 1002C). Note that while in this embodiment the MAPclient 30-1 sends the complete user profile of the user 20-1 to the MAPserver 12, in an alternative embodiment, the MAP client 30-1 may filterthe user profile of the user 20-1 according to criteria specified by theuser 20-1. For example, the user profile of the user 20-1 may includedemographic information, general interests, music interests, and movieinterests, and the user 20-1 may specify that the demographicinformation or some subset thereof is to be filtered, or removed, beforesending the user profile to the MAP server 12.

Upon receiving the user profile of the user 20-1 from the MAP client30-1 of the mobile device 18-1, the profile manager 52 of the MAP server12 processes the user profile (step 1002D). More specifically, in thepreferred embodiment, the profile manager 52 includes social networkhandlers for the social network services supported by the MAP server 12.Thus, for example, if the MAP server 12 supports user profiles fromFacebook, MySpace, and LinkedIN, the profile manager 52 may include aFacebook handler, a MySpace handler, and a LinkedIN handler. The socialnetwork handlers process user profiles to generate user profiles for theMAP server 12 that include lists of keywords for each of a number ofprofile categories. The profile categories may be the same for each ofthe social network handlers or different for each of the social networkhandlers. Thus, for this example assume that the user profile of theuser 20-1 is from Facebook. The profile manager 52 uses a Facebookhandler to process the user profile of the user 20-1 to map the userprofile of the user 20-1 from Facebook to a user profile for the MAPserver 12 including lists of keywords for a number of predefined profilecategories. For example, for the Facebook handler, the profilecategories may be a demographic profile category, a social interactionprofile category, a general interests profile category, a musicinterests profile category, and a movie interests profile category. Assuch, the user profile of the user 20-1 from Facebook may be processedby the Facebook handler of the profile manager 52 to create a list ofkeywords such as, for example, liberal, High School Graduate, 35-44,College Graduate, etc. for the demographic profile category, a list ofkeywords such as Seeking Friendship for the social interaction profilecategory, a list of keywords such as politics, technology, photography,books, etc. for the general interests profile category, a list ofkeywords including music genres, artist names, album names, or the likefor the music interests profile category, and a list of keywordsincluding movie titles, actor or actress names, director names, movegenres, or the like for the movie interests profile category. In oneembodiment, the profile manager 52 may use natural language processingor semantic analysis. For example, if the Facebook user profile of theuser 20-1 states that the user 20-1 is 20 years old, semantic analysismay result in the keyword of 18-24 years old being stored in the userprofile of the user 20-1 for the MAP server 12.

After processing the user profile of the user 20-1, the profile manager52 of the MAP server 12 stores the resulting user profile for the user20-1 (step 1002E). More specifically, in one embodiment, the MAP server12 stores user records for the users 20-1 through 20-N in the datastore64 (FIG. 2). The user profile of the user 20-1 is stored in the userrecord of the user 20-1. The user record of the user 20-1 includes aunique identifier of the user 20-1, the user profile of the user 20-1,and, as discussed below, a current location of the user 20-1. Note thatthe user profile of the user 20-1 may be updated as desired. Forexample, in one embodiment, the user profile of the user 20-1 is updatedby repeating step 1002 each time the user 20-1 activates the MAPapplication 32-1.

Note that while the discussion herein focuses on an embodiment where theuser profiles of the users 20-1 through 20-N are obtained from the oneor more profile servers 14, the user profiles of the users 20-1 through20-N may be obtained in any desired manner. For example, in onealternative embodiment, the user 20-1 may identify one or more favoritewebsites. The profile manager 52 of the MAP server 12 may then crawl theone or more favorite websites of the user 20-1 to obtain keywordsappearing in the one or more favorite websites of the user 20-1. Thesekeywords may then be stored as the user profile of the user 20-1.

At some point, a process is performed such that a current location ofthe mobile device 18-1 and thus a current location of the user 20-1 isobtained by the MAP server 12 (step 1004). In this embodiment, the MAPapplication 32-1 of the mobile device 18-1 obtains the current locationof the mobile device 18-1 from the location function 36-1 of the mobiledevice 18-1. The MAP application 32-1 then provides the current locationof the mobile device 18-1 to the MAP client 30-1, and the MAP client30-1 then provides the current location of the mobile device 18-1 to theMAP server 12 (step 1004A). Note that step 1004A may be repeatedperiodically or in response to a change in the current location of themobile device 18-1 in order for the MAP application 32-1 to providelocation updates for the user 20-1 to the MAP server 12.

In response to receiving the current location of the mobile device 18-1,the location manager 54 of the MAP server 12 stores the current locationof the mobile device 18-1 as the current location of the user 20-1 (step1004B). More specifically, in one embodiment, the current location ofthe user 20-1 is stored in the user record of the user 20-1 maintainedin the datastore 64 of the MAP server 12. Note that only the currentlocation of the user 20-1 is stored in the user record of the user 20-1.In this manner, the MAP server 12 maintains privacy for the user 20-1since the MAP server 12 does not maintain a historical record of thelocation of the user 20-1. As discussed below in detail, historical datamaintained by the MAP server 12 is anonymized in order to maintain theprivacy of the users 20-1 through 20-N.

In addition to storing the current location of the user 20-1, thelocation manager 54 sends the current location of the user 20-1 to thelocation server 16 (step 1004C). In this embodiment, by providinglocation updates to the location server 16, the MAP server 12 in returnreceives location updates for the user 20-1 from the location server 16.This is particularly beneficial when the mobile device 18-1 does notpermit background processes, which is the case for the Apple® iPhone. Assuch, if the mobile device 18-1 is an Apple® iPhone or similar devicethat does not permit background processes, the MAP application 32-1 willnot be able to provide location updates for the user 20-1 to the MAPserver 12 unless the MAP application 32-1 is active.

Therefore, when the MAP application 32-1 is not active, otherapplications running on the mobile device 18-1 (or some other device ofthe user 20-1) may directly or indirectly provide location updates tothe location server 16 for the user 20-1. This is illustrated in step1006 where the location server 16 receives a location update for theuser 20-1 directly or indirectly from another application running on themobile device 18-1 or an application running on another device of theuser 20-1 (step 1006A). The location server 16 then provides thelocation update for the user 20-1 to the MAP server 12 (step 1006B). Inresponse, the location manager 54 updates and stores the currentlocation of the user 20-1 in the user record of the user 20-1 (step1006C). In this manner, the MAP server 12 is enabled to obtain locationupdates for the user 20-1 even when the MAP application 32-1 is notactive at the mobile device 18-1.

FIG. 5 illustrates the operation of the system 10 of FIG. 1 to providethe user profile of the user 20-1 of the mobile device 18-1 according toanother embodiment of the present disclosure. This discussion is equallyapplicable to user profiles of the other users 20-2 through 20-N of theother mobile devices 18-2 through 18-N. First, an authentication processis performed (step 1100). For authentication, in this embodiment, themobile device 18-1 authenticates with the MAP server 12 (step 1100A),and the MAP server 12 authenticates with the profile server 14 (step1100B). Preferably, authentication is performed using OpenID or similartechnology. However, authentication may alternatively be performed usingseparate credentials (e.g., username and password) of the user 20-1 foraccess to the MAP server 12 and the profile server 14. Assuming thatauthentication is successful, the profile server 14 returns anauthentication succeeded message to the MAP server 12 (step 1100C), andthe MAP server 12 returns an authentication succeeded message to the MAPclient 30-1 of the mobile device 18-1 (step 1100D).

At some point after authentication is complete, a user profile processis performed such that a user profile of the user 20-1 is obtained fromthe profile server 14 and delivered to the MAP server 12 (step 1102). Inthis embodiment, the profile manager 52 of the MAP server 12 sends aprofile request to the profile server 14 (step 1102A). In response, theprofile server 14 returns the user profile of the user 20-1 to theprofile manager 52 of the MAP server 12 (step 1102B). Note that while inthis embodiment the profile server 14 returns the complete user profileof the user 20-1 to the MAP server 12, in an alternative embodiment, theprofile server 14 may return a filtered version of the user profile ofthe user 20-1 to the MAP server 12. The profile server 14 may filter theuser profile of the user 20-1 according to criteria specified by theuser 20-1. For example, the user profile of the user 20-1 may includedemographic information, general interests, music interests, and movieinterests, and the user 20-1 may specify that the demographicinformation or some subset thereof is to be filtered, or removed, beforesending the user profile to the MAP server 12.

Upon receiving the user profile of the user 20-1, the profile manager 52of the MAP server 12 processes to the user profile (step 1102C). Morespecifically, as discussed above, in the preferred embodiment, theprofile manager 52 includes social network handlers for the socialnetwork services supported by the MAP server 12. The social networkhandlers process user profiles to generate user profiles for the MAPserver 12 that include lists of keywords for each of a number of profilecategories. The profile categories may be the same for each of thesocial network handlers or different for each of the social networkhandlers.

After processing the user profile of the user 20-1, the profile manager52 of the MAP server 12 stores the resulting user profile for the user20-1 (step 1102D). More specifically, in one embodiment, the MAP server12 stores user records for the users 20-1 through 20-N in the datastore64 (FIG. 2). The user profile of the user 20-1 is stored in the userrecord of the user 20-1. The user record of the user 20-1 includes aunique identifier of the user 20-1, the user profile of the user 20-1,and, as discussed below, a current location of the user 20-1. Note thatthe user profile of the user 20-1 may be updated as desired. Forexample, in one embodiment, the user profile of the user 20-1 is updatedby repeating step 1102 each time the user 20-1 activates the MAPapplication 32-1.

Note that the while the discussion herein focuses on an embodiment wherethe user profiles of the users 20-1 through 20-N are obtained from theone or more profile servers 14, the user profiles of the users 20-1through 20-N may be obtained in any desired manner. For example, in onealternative embodiment, the user 20-1 may identify one or more favoritewebsites. The profile manager 52 of the MAP server 12 may then crawl theone or more favorite websites of the user 20-1 to obtain keywordsappearing in the one or more favorite websites of the user 20-1. Thesekeywords may then be stored as the user profile of the user 20-1.

At some point, a process is performed such that a current location ofthe mobile device 18-1 and thus a current location of the user 20-1 isobtained by the MAP server 12 (step 1104). In this embodiment, the MAPapplication 32-1 of the mobile device 18-1 obtains the current locationof the mobile device 18-1 from the location function 36-1 of the mobiledevice 18-1. The MAP application 32-1 then provides the current locationof the user 20-1 of the mobile device 18-1 to the location server 16(step 1104A). Note that step 1104A may be repeated periodically or inresponse to changes in the location of the mobile device 18-1 in orderto provide location updates for the user 20-1 to the MAP server 12. Thelocation server 16 then provides the current location of the user 20-1to the MAP server 12 (step 1104B). The location server 16 may providethe current location of the user 20-1 to the MAP server 12 automaticallyin response to receiving the current location of the user 20-1 from themobile device 18-1 or in response to a request from the MAP server 12.

In response to receiving the current location of the mobile device 18-1,the location manager 54 of the MAP server 12 stores the current locationof the mobile device 18-1 as the current location of the user 20-1 (step1104C). More specifically, in one embodiment, the current location ofthe user 20-1 is stored in the user record of the user 20-1 maintainedin the datastore 64 of the MAP server 12. Note that only the currentlocation of the user 20-1 is stored in the user record of the user 20-1.In this manner, the MAP server 12 maintains privacy for the user 20-1since the MAP server 12 does not maintain a historical record of thelocation of the user 20-1. As discussed below in detail, historical datamaintained by the MAP server 12 is anonymized in order to maintain theprivacy of the users 20-1 through 20-N.

As discussed above, the use of the location server 16 is particularlybeneficial when the mobile device 18-1 does not permit backgroundprocesses, which is the case for the Apple® iPhone. As such, if themobile device 18-1 is an Apple® iPhone or similar device that does notpermit background processes, the MAP application 32-1 will not providelocation updates for the user 20-1 to the location server 16 unless theMAP application 32-1 is active. However, other applications running onthe mobile device 18-1 (or some other device of the user 20-1) mayprovide location updates to the location server 16 for the user 20-1when the MAP application 32-1 is not active. This is illustrated in step1106 where the location server 16 receives a location update for theuser 20-1 from another application running on the mobile device 18-1 oran application running on another device of the user 20-1 (step 1106A).The location server 16 then provides the location update for the user20-1 to the MAP server 12 (step 1106B). In response, the locationmanager 54 updates and stores the current location of the user 20-1 inthe user record of the user 20-1 (step 1106C). In this manner, the MAPserver 12 is enabled to obtain location updates for the user 20-1 evenwhen the MAP application 32-1 is not active at the mobile device 18-1.

Using the current locations of the users 20-1 through 20-N and the userprofiles of the users 20-1 through 20-N, the MAP server 12 can provide anumber of features. A first feature that may be provided by the MAPserver 12 is historical storage of anonymized user profile data bylocation. This historical storage of anonymized user profile data bylocation is performed by the history manager 56 of the MAP server 12.More specifically, as illustrated in FIG. 6, in the preferredembodiment, the history manager 56 maintains lists of users located in anumber of geographic regions, or “location buckets.” Preferably, thelocation buckets are defined by floor(latitude, longitude) to a desiredresolution. The higher the resolution, the smaller the size of thelocation buckets. For example, in one embodiment, the location bucketsare defined by floor(latitude, longitude) to a resolution of1/10,000^(th) of a degree such that the lower left-hand corners of thesquares illustrated in FIG. 6 are defined by the floor(latitude,longitude) values at a resolution of 1/10,000^(th) of a degree. In theexample of FIG. 6, users are represented as dots, and location buckets72 through 88 have lists of 1, 3, 2, 1, 1, 2, 1, 2, and 3 users,respectively.

As discussed below in detail, at a predetermined time interval such as,for example, 15 minutes, the history manager 56 makes a copy of thelists of users in the location buckets, anonymizes the user profiles ofthe users in the lists to provide anonymized user profile data for thecorresponding location buckets, and stores the anonymized user profiledata in a number of history objects. In one embodiment, a history objectis stored for each location bucket having at least one user. In anotherembodiment, a quadtree algorithm is used to efficiently create historyobjects for geographic regions (i.e., groups of one or more adjoininglocation buckets).

FIG. 7 graphically illustrates a scenario where a user moves from onelocation bucket to another, namely, from the location bucket 74 to thelocation bucket 76. As discussed below in detail, assuming that themovement occurs during the time interval between persistence of thehistorical data by the history manager 56, the user is included on boththe list for the location bucket 74 and the list for the location bucket76. However, the user is flagged or otherwise marked as inactive for thelocation bucket 74 and active for the location bucket 76. As discussedbelow, after making a copy of the lists for the location buckets to beused to persist the historical data, users flagged as inactive areremoved from the lists of users for the location buckets. Thus, in sum,once a user moves from the location bucket 74 to the location bucket 76,the user remains in the list for the location bucket 74 until thepredetermined time interval has expired and the anonymized user profiledata is persisted. The user is then removed from the list for thelocation bucket 74.

FIG. 8 is a flow chart illustrating the operation of a foreground“bucketization” process performed by the history manager 56 to maintainthe lists of users for location buckets according to one embodiment ofthe present disclosure. First, the history manager 56 receives alocation update for a user (step 1200). For this discussion, assume thatthe location update is received for the user 20-1. The history manager56 then determines a location bucket corresponding to the updatedlocation (i.e., the current location) of the user 20-1 (step 1202). Inthe preferred embodiment, the location of the user 20-1 is expressed aslatitude and longitude coordinates, and the history manager 56determines the location bucket by determining floor values of thelatitude and longitude coordinates, which can be written asfloor(latitude, longitude) at a desired resolution. As an example, ifthe latitude and longitude coordinates for the location of the user 20-1are 32.24267381553987 and −111.9249213502935, respectively, and thefloor values are to be computed to a resolution of 1/10,000^(th) of adegree, then the floor values for the latitude and longitude coordinatesare 32.2426 and −111.9249. The floor values for the latitude andlongitude coordinates correspond to a particular location bucket.

After determining the location bucket for the location of the user 20-1,the history manager 56 determines whether the user 20-1 is new to thelocation bucket (step 1204). In other words, the history manager 56determines whether the user 20-1 is already on the list of users for thelocation bucket. If the user 20-1 is new to the location bucket, thehistory manager 56 creates an entry for the user 20-1 in the list ofusers for the location bucket (step 1206). Returning to step 1204, ifthe user 20-1 is not new to the location bucket, the history manager 56updates the entry for the user 20-1 in the list of users for thelocation bucket (step 1208). At this point, whether proceeding from step1206 or 1208, the user 20-1 is flagged as active in the list of usersfor the location bucket (step 1210).

The history manager 56 then determines whether the user 20-1 has movedfrom another location bucket (step 1212). More specifically, the historymanager 56 determines whether the user 20-1 is included in the list ofusers for another location bucket and is currently flagged as active inthat list. If the user 20-1 has not moved from another location bucket,the process proceeds to step 1216. If the user 20-1 has moved fromanother location bucket, the history manager 56 flags the user 20-1 asinactive in the list of users for the other location bucket from whichthe user 20-1 has moved (step 1214).

At this point, whether proceeding from step 1212 or 1214, the historymanager 56 determines whether it is time to persist (step 1216). Morespecifically, as mentioned above, the history manager 56 operates topersist history objects at a predetermined time interval such as, forexample, every 15 minutes. Thus, the history manager 56 determines thatit is time to persist if the predetermined time interval has expired. Ifit is not time to persist, the process returns to step 1200 and isrepeated for a next received location update, which will typically befor another user. If it is time to persist, the history manager 56creates a copy of the lists of users for the location buckets and passesthe copy of the lists to an anonymization and storage process (step1218). In this embodiment, the anonymization and storage process is aseparate process performed by the history manager 56. The historymanager 56 then removes inactive users from the lists of users for thelocation buckets (step 1220). The process then returns to step 300 andis repeated for a next received location update, which will typically befor another user.

FIG. 9 is a flow chart illustrating the anonymization and storageprocess performed by the history manager 56 at the predetermined timeinterval according to one embodiment of the present disclosure. First,the anonymization and storage process receives the copy of the lists ofusers for the location buckets passed to the anonymization and storageprocess by the bucketization process of FIG. 8 (step 1300). Next,anonymization is performed for each of the location buckets having atleast one user in order to provide anonymized user profile data for thelocation buckets (step 1302). Anonymization prevents connectinginformation stored in the history objects stored by the history manager56 back to the users 20-1 through 20-N or at least substantiallyincreases a difficulty of connecting information stored in the historyobjects stored by the history manager 56 back to the users 20-1 through20-N. Lastly, the anonymized user profile data for the location bucketsis stored in a number of history objects (step 1304). In one embodiment,a separate history object is stored for each of the location buckets,where the history object of a location bucket includes the anonymizeduser profile data for the location bucket. In another embodiment, asdiscussed below, a quadtree algorithm is used to efficiently store theanonymized user profile data in a number of history objects such thateach history object stores the anonymized user profile data for one ormore location buckets.

FIG. 10 graphically illustrates one embodiment of the anonymizationprocess of step 1302 of FIG. 9. In this embodiment, anonymization isperformed by creating anonymous user records for the users in the listsof users for the location buckets. The anonymous user records are notconnected back to the users 20-1 through 20-N. More specifically, asillustrated in FIG. 10, each user in the lists of users for the locationbuckets has a corresponding user record 90. The user record 90 includesa unique user identifier (ID) for the user, the current location of theuser, and the user profile of the user. The user profile includeskeywords for each of a number of profile categories, which are stored incorresponding profile category records 92-1 through 92-M. Each of theprofile category records 92-1 through 92-M includes a user ID for thecorresponding user which may be the same user ID used in the user record90, a category ID, and a list of keywords for the profile category.

For anonymization, an anonymous user record 94 is created from the userrecord 90. In the anonymous user record 94, the user ID is replaced witha new user ID that is not connected back to the user, which is alsoreferred to herein as an anonymous user ID. This new user ID isdifferent than any other user ID used for anonymous user records createdfrom the user record of the user for any previous or subsequent timeperiods. In this manner, anonymous user records for a single usercreated over time cannot be linked to one another.

In addition, anonymous profile category records 96-1 through 96-M arecreated for the profile category records 92-1 through 92-M. In theanonymous profile category records 96-1 through 96-M, the user ID isreplaced with a new user ID, which may be the same new user ID includedin the anonymous user record 94. The anonymous profile category records96-1 through 96-M include the same category IDs and lists of keywords asthe corresponding profile category records 92-1 through 92-M. Note thatthe location of the user is not stored in the anonymous user record 94.With respect to location, it is sufficient that the anonymous userrecord 94 is linked to a location bucket.

In another embodiment, the history manager 56 performs anonymization ina manner similar to that described above with respect to FIG. 10.However, in this embodiment, the profile category records for the groupof users in a location bucket, or the group of users in a number oflocation buckets representing a node in a quadtree data structure (seebelow), may be selectively randomized among the anonymous user recordsof those users. In other words, each anonymous user record would have auser profile including a selectively randomized set of profile categoryrecords (including keywords) from a cumulative list of profile categoryrecords for all of the users in the group.

In yet another embodiment, rather than creating anonymous user records94 for the users in the lists maintained for the location buckets, thehistory manager 56 may perform anonymization by storing an aggregateuser profile for each location bucket, or each group of location bucketsrepresenting a node in a quadtree data structure (see below). Theaggregate user profile may include a list of all keywords andpotentially the number of occurrences of each keyword in the userprofiles of the corresponding group of users. In this manner, the datastored by the history manager 56 is not connected back to the users 20-1through 20-N.

FIG. 11 is a flow chart illustrating the storing step (step 1304) ofFIG. 9 in more detail according to one embodiment of the presentdisclosure. First, the history manager 56 processes the location bucketsusing a quadtree algorithm to produce a quadtree data structure, whereeach node of the quadtree data structure includes one or more of thelocation buckets having a combined number of users that is at most apredefined maximum number of users (step 1400). The history manager 56then stores a history object for each node in the quadtree datastructure having at least one user (step 1402).

Each history object includes location information, timing information,data, and quadtree data structure information. The location informationincluded in the history object defines a combined geographic area of thelocation bucket(s) forming the corresponding node of the quadtree datastructure. For example, the location information may be latitude andlongitude coordinates for a northeast corner of the combined geographicarea of the node of the quadtree data structure and a southwest cornerof the combined geographic area for the node of the quadtree datastructure. The timing information includes information defining a timewindow for the history object, which may be, for example, a start timefor the corresponding time interval and an end time for thecorresponding time interval. The data includes the anonymized userprofile data for the users in the list(s) maintained for the locationbucket(s) forming the node of the quadtree data structure for which thehistory object is stored. In addition, the data may include a totalnumber of users in the location bucket(s) forming the node of thequadtree data structure. Lastly, the quadtree data structure informationincludes information defining a quadtree depth of the node in thequadtree data structure.

FIG. 12 is a flow chart illustrating a quadtree algorithm that may beused to process the location buckets to form the quadtree data structurein step 1400 of FIG. 11 according to one embodiment of the presentdisclosure. Initially, a geographic area served by the MAP server 12 isdivided into a number of geographic regions, each including multiplelocation buckets. These geographic regions are also referred to hereinas base quadtree regions. The geographic area served by the MAP server12 may be, for example, a city, a state, a country, or the like.Further, the geographic area may be the only geographic area served bythe MAP server 12 or one of a number of geographic areas served by theMAP server 12. Preferably, the base quadtree regions have a size of2^(n)×2^(n) location buckets, where n is an integer greater than orequal to 1.

In order to form the quadtree data structure, the history manager 56determines whether there are any more base quadtree regions to process(step 1500). If there are more base quadtree regions to process, thehistory manager 56 sets a current node to the next base quadtree regionto process, which for the first iteration is the first base quadtreeregion (step 1502). The history manager 56 then determines whether thenumber of users in the current node is greater than a predefined maximumnumber of users and whether a current quadtree depth is less than amaximum quadtree depth (step 1504). In one embodiment, the maximumquadtree depth may be reached when the current node corresponds to asingle location bucket. However, the maximum quadtree depth may be setsuch that the maximum quadtree depth is reached before the current nodereaches a single location bucket.

If the number of users in the current node is greater than thepredefined maximum number of users and the current quadtree depth isless than a maximum quadtree depth, the history manager 56 creates anumber of child nodes for the current node (step 1506). Morespecifically, the history manager 56 creates a child node for eachquadrant of the current node. The users in the current node are thenassigned to the appropriate child nodes based on the location buckets inwhich the users are located (step 1508), and the current node is thenset to the first child node (step 1510). At this point, the processreturns to step 1504 and is repeated.

Once the number of users in the current node is not greater than thepredefined maximum number of users or the maximum quadtree depth hasbeen reached, the history manager 56 determines whether the current nodehas any more sibling nodes (step 1512). Sibling nodes are child nodes ofthe same parent node. If so, the history manager 56 sets the currentnode to the next sibling node of the current node (step 1514), and theprocess returns to step 1504 and is repeated. Once there are no moresibling nodes to process, the history manager 56 determines whether thecurrent node has a parent node (step 1516). If so, since the parent nodehas already been processed, the history manager 56 determines whetherthe parent node has any sibling nodes that need to be processed (step1518). If the parent node has any sibling nodes that need to beprocessed, the history manager 56 sets the next sibling node of theparent node to be processed as the current node (step 1520). From thispoint, the process returns to step 1504 and is repeated. Returning tostep 1516, if the current node does not have a parent node, the processreturns to step 1500 and is repeated until there are no more basequadtree regions to process. Once there are no more base quadtreeregions to process, the finished quadtree data structure is returned tothe process of FIG. 11 such that the history manager 56 can then storethe history objects for nodes in the quadtree data structure having atleast one user (step 1522).

FIGS. 13A through 13E graphically illustrate the process of FIG. 12 forthe generation of the quadtree data structure for one exemplary basequadtree region 98. FIG. 13A illustrates the base quadtree region 98. Asillustrated, the base quadtree region 98 is an 8×8 square of locationbuckets, where each of the small squares represents a location bucket.First, the history manager 56 determines whether the number of users inthe base quadtree region 98 is greater than the predetermined maximumnumber of users. In this example, the predetermined maximum number ofusers is 3. Since the number of users in the base quadtree region 98 isgreater than 3, the history manager 56 divides the base quadtree region98 into four child nodes 100-1 through 100-4, as illustrated in FIG.13B.

Next, the history manager 56 determines whether the number of users inthe child node 100-1 is greater than the predetermined maximum, whichagain for this example is 3. Since the number of users in the child node100-1 is greater than 3, the history manager 56 divides the child node100-1 into four child nodes 102-1 through 102-4, as illustrated in FIG.13C. The child nodes 102-1 through 102-4 are children of the child node100-1. The history manager 56 then determines whether the number ofusers in the child node 102-1 is greater than the predetermined maximumnumber of users, which again is 3. Since there are more than 3 users inthe child node 102-1, the history manager 56 further divides the childnode 102-1 into four child nodes 104-1 through 104-N, as illustrated inFIG. 13D.

The history manager 56 then determines whether the number of users inthe child node 104-1 is greater than the predetermined maximum number ofusers, which again is 3. Since the number of users in the child node104-1 is not greater than the predetermined maximum number of users, thechild node 104-1 is identified as a node for the finished quadtree datastructure, and the history manager 56 proceeds to process the siblingnodes of the child node 104-1, which are the child nodes 104-2 through104-4. Since the number of users in each of the child nodes 104-2through 104-4 is less than the predetermined maximum number of users,the child nodes 104-2 through 104-4 are also identified as nodes for thefinished quadtree data structure.

Once the history manager 56 has finished processing the child nodes104-1 through 104-4, the history manager 56 identifies the parent nodeof the child nodes 104-1 through 104-4, which in this case is the childnode 102-1. The history manager 56 then processes the sibling nodes ofthe child node 102-1, which are the child nodes 102-2 through 102-4. Inthis example, the number of users in each of the child nodes 102-2through 102-4 is less than the predetermined maximum number of users. Assuch, the child nodes 102-2 through 102-4 are identified as nodes forthe finished quadtree data structure.

Once the history manager 56 has finished processing the child nodes102-1 through 102-4, the history manager 56 identifies the parent nodeof the child nodes 102-1 through 102-4, which in this case is the childnode 100-1. The history manager 56 then processes the sibling nodes ofthe child node 100-1, which are the child nodes 100-2 through 100-4.More specifically, the history manager 56 determines that the child node100-2 includes more than the predetermined maximum number of users and,as such, divides the child node 100-2 into four child nodes 106-1through 106-4, as illustrated in FIG. 13E. Because the number of usersin each of the child nodes 106-1 through 106-4 is not greater than thepredetermined maximum number of users, the child nodes 106-1 through106-4 are identified as nodes for the finished quadtree data structure.Then, the history manager 56 proceeds to process the child nodes 100-3and 100-4. Since the number of users in each of the child nodes 100-3and 100-4 is not greater than the predetermined maximum number of users,the child nodes 100-3 and 100-4 are identified as nodes for the finishedquadtree data structure. Thus, at completion, the quadtree datastructure for the base quadtree region 98 includes the child nodes 104-1through 104-4, the child nodes 102-2 through 102-4, the child nodes106-1 through 106-4, and the child nodes 100-3 and 100-4, as illustratedin FIG. 13E.

As discussed above, the history manager 56 stores a history object foreach of the nodes in the quadtree data structure including at least oneuser. As such, in this example, the history manager 56 stores historyobjects for the child nodes 104-2 and 104-3, the child nodes 102-2 and102-4, the child nodes 106-1 and 106-4, and the child node 100-3.However, no history objects are stored for the nodes that do not haveany users (i.e., the child nodes 104-1 and 104-4, the child node 102-3,the child nodes 106-2 and 106-3, and the child node 100-4).

FIG. 14 illustrates the operation of the system 10 of FIG. 1 wherein amobile device is enabled to request and receive historical data from theMAP server 12 according to one embodiment of the present disclosure. Asillustrated, in this embodiment, the MAP application 32-1 of the mobiledevice 18-1 sends a historical request to the MAP client 30-1 of themobile device 18-1 (step 1600). In one embodiment, the historicalrequest identifies either a POI or an AOI and a time window. A POI is ageographic point whereas an AOI is a geographic area. In one embodiment,the historical request is for a POI and a time window, where the POI isa POI corresponding to the current location of the user 20-1, a POIselected from a list of POIs defined by the user 20-1 of the mobiledevice 18-1, a POI selected from a list of POIs defined by the MAPapplication 32-1 or the MAP server 12, a POI selected by the user 20-1from a map, a POI implicitly defined via a separate application (e.g.,POI is implicitly defined as the location of the nearest Starbuckscoffee house in response to the user 20-1 performing a Google search for“Starbucks”), or the like. If the POI is selected from a list of POIs,the list of POIs may include static POIs which may be defined by streetaddresses or latitude and longitude coordinates, dynamic POIs which maybe defined as the current locations of one or more friends of the user20-1, or both.

In another embodiment, the historical request is for an AOI and a timewindow, where the AOI may be an AOI of a geographic area of a predefinedshape and size centered at the current location of the user 20-1, an AOIselected from a list of AOIs defined by the user 20-1, an AOI selectedfrom a list of AOIs defined by the MAP application 32-1 or the MAPserver 12, an AOI selected by the user 20-1 from a map, an AOIimplicitly defined via a separate application (e.g., AOI is implicitlydefined as an area of a predefined shape and size centered at thelocation of the nearest Starbucks coffee house in response to the user20-1 performing a Google search for “Starbucks”), or the like. If theAOI is selected from a list of AOIs, the list of AOIs may include staticAOIs, dynamic AOIs which may be defined as areas of a predefined shapeand size centered at the current locations of one or more friends of theuser 20-1, or both. Note that the POI or AOI of the historical requestmay be selected by the user 20-1 via the MAP application 32-1. In yetanother embodiment, the MAP application 32-1 automatically uses thecurrent location of the user 20-1 as the POI or as a center point for anAOI of a predefined shape and size.

The time window for the historical request may be relative to thecurrent time. For example, the time window may be the last hour, thelast day, the last week, the last month, or the like. Alternatively, thetime window may be an arbitrary time window selected by the user 20-1such as, for example, yesterday from 7 pm-9 pm, last Friday, last week,or the like. Note that while in this example the historical requestincludes a single POI or AOI and a single time window, the historicalrequest may include multiple POIs or AOIs and/or multiple time windows.

In one embodiment, the historical request is made in response to userinput from the user 20-1 of the mobile device 18-1. For instance, in oneembodiment, the user 20-1 selects either a POI or an AOI and a timewindow and then instructs the MAP application 32-1 to make thehistorical request by, for example, selecting a corresponding button ona graphical user interface. In another embodiment, the historicalrequest is made automatically in response to some event such as, forexample, opening the MAP application 32-1.

Upon receiving the historical request from the MAP application 32-1, theMAP client 30-1 forwards the historical request to the MAP server 12(step 1602). Note that the MAP client 30-1 may, in some cases, processthe historical request from the MAP application 32-1 before forwardingthe historical request to the MAP server 12. For example, if thehistorical request from the MAP application 32-1 is for multiplePOIs/AOIs and/or for multiple time windows, the MAP client 30-1 mayprocess the historical request from the MAP application 32-1 to producemultiple historical requests to be sent to the MAP server 12. Forinstance, a separate historical request may be produced for each POI/AOIand time window combination. However, for this discussion, thehistorical request is for a single POI or AOI for a single time window.

Upon receiving the historical request from the MAP client 30-1, the MAPserver 12 processes the historical request (step 1604). Morespecifically, the historical request is processed by the history manager56 of the MAP server 12. First, the history manager 56 obtains historyobjects that are relevant to the historical request from the datastore64 of the MAP server 12. The relevant history objects are those recordedfor locations relevant to the POI or AOI and the time window for thehistorical request. The history manager 56 then processes the relevanthistory objects to provide historical aggregate profile data for the POIor AOI in a time context and/or a geographic context. In thisembodiment, the historical aggregate profile data is based on the userprofiles of the anonymous user records in the relevant history objectsas compared to the user profile of the user 20-1 or a select subsetthereof. In another embodiment, the historical aggregate profile data isbased on the user profiles of the anonymous user records in the relevanthistory objects as compared to a target user profile defined orotherwise specified by the user 20-1.

As discussed below in detail, for the time context, the history manager56 divides the time window for the historical request into a number oftime bands. Each time band is a fragment of the time window. Then, foreach time band, the history manager 56 identifies a subset of therelevant history objects that are relevant to the time band (i.e.,history objects recorded for time periods within the time band or thatoverlap the time band) and generates an aggregate profile for each ofthose history objects based on the user profiles of the anonymous userrecords in the history objects and the user profile, or a select subsetof the user profile, of the user 20-1. Then, the history manager 56averages or otherwise combines the aggregate profiles for the historyobjects relevant to the time band. The resulting data for the time bandsforms historical aggregate profile data that is to be returned to theMAP client 30-1, as discussed below.

For the geographic context, the history manager 56 generates an averageaggregate profile for each of a number of grids surrounding the POI orwithin the AOI. More specifically, history objects relevant to the POIor the AOI and the time window of the historical request are obtained.Then, the user profiles of the anonymous users in the relevant historyobjects are used to generate average aggregate profiles for a number ofgrids, or geographic regions, at or surrounding the POI or the AOI.These average aggregate profiles for the grids form historical aggregateprofile data that is to be returned to the MAP client 30-1, as discussedbelow.

Once the MAP server 12 has processed the historical request, the MAPserver 12 returns the resulting historical aggregate profile data to theMAP client 30-1 (step 1606). As discussed above, the historicalaggregate profile data may be in a time context or a geographic context.In an alternative embodiment, the data returned to the MAP client 30-1may be raw historical data. The raw historical data may be the relevanthistory objects or data from the relevant history objects such as, forexample, the user records in the relevant history objects, the userprofiles of the anonymous user records in the relevant history objects,or the like.

Upon receiving the historical aggregate profile data, the MAP client30-1 passes the historical aggregate profile data to the MAP application32-1 (step 1608). Note that in an alternative embodiment where the datareturned by the MAP server 12 is raw historical data, the MAP client30-1 may process the raw historical data to provide desired data. Forexample, the MAP client 30-1 may process the raw historical data inorder to generate average aggregate profiles for time bands within thetime window of the historical request and/or to generate averageaggregate profiles for regions near the POI or within the AOI of thehistorical request in a manner similar to that described above. The MAPapplication 32-1 then presents the historical aggregate profile data tothe user 20-1 (step 1610).

FIGS. 15A and 15B illustrate a flow chart for a process for generatinghistorical aggregate profile data in a time context according to oneembodiment of the present disclosure. First, upon receiving a historicalrequest, the history manager 56 establishes a bounding box for thehistorical request based on the POI or the AOI for the historicalrequest (step 1700). Note that while a bounding box is used in thisexample, other geographic shapes may be used to define a bounding regionfor the historical request (e.g., a bounding circle). In thisembodiment, the historical request is from a mobile device of arequesting user, which in this example is the user 20-1. If thehistorical request is for a POI, the bounding box is a geographic regioncorresponding to or surrounding the POI. For example, the bounding boxmay be a square geographic region of a predefined size centered on thePOI. If the historical request is for an AOI, the bounding box is theAOI. In addition to establishing the bounding box, the history manager56 establishes a time window for the historical request (step 1702). Forexample, if the historical request is for the last week and the currentdate and time are Sep. 17, 2009 at 10:00 pm, the history manager 56 maygenerate the time window as Sep. 10, 2009 at 10:00 pm through Sep. 17,2009 at 10:00 pm.

Next, the history manager 56 obtains history objects relevant to thebounding box and the time window for the historical request from thedatastore 64 of the MAP server 12 (step 1704). The relevant historyobjects are history objects recorded for time periods within orintersecting the time window and for locations, or geographic areas,within or intersecting the bounding box for the historical request. Thehistory manager 56 also determines an output time band size (step 1706).In one exemplary embodiment, the output time band size is 1/100^(th) ofthe amount of time from the start of the time window to the end of thetime window for the historical request. For example, if the amount oftime in the time window for the historical request is one week, theoutput time band size may be set to 1/100^(th) of a week, which is 1.68hours or 1 hour and 41 minutes.

The history manager 56 then sorts the relevant history objects into theappropriate output time bands of the time window for the historicalrequest. More specifically, in this embodiment, the history manager 56creates an empty list for each of output time band of the time window(step 1708). Then, the history manager 56 gets the next history objectfrom the history objects identified in step 1704 as being relevant tothe historical request (step 1710) and adds that history object to thelist(s) for the appropriate output time band(s) (step 1712). Note thatif the history object is recorded for a time period that overlaps two ormore of the output time bands, then the history object may be added toall of the output time bands to which the history object is relevant.The history manager 56 then determines whether there are more relevanthistory objects to sort into the output time bands (step 1714). If so,the process returns to step 1710 and is repeated until all of therelevant history objects have been sorted into the appropriate outputtime bands.

Once sorting is complete, the history manager 56 determines anequivalent depth of the bounding box (D_(BB)) within the quadtree datastructures used to store the history objects (step 1716). Morespecifically, the area of the base quadtree region (e.g., the basequadtree region 98) is referred to as A_(BASE). Then, at each depth ofthe quadtree, the area of the corresponding quadtree nodes is(¼)^(D)*A_(BASE). In other words, the area of a child node is ¼^(th) ofthe area of the parent node of that child node. The history manager 56determines the equivalent depth of the bounding box (D_(BB)) bydetermining a quadtree depth at which the area of the correspondingquadtree nodes most closely matches an area of the bounding box(A_(BB)).

Note that equivalent quadtree depth of the bounding box (D_(BB))determined in step 1716 is used below in order to efficiently determinethe ratios of the area of the bounding box (A_(BB)) to areas of therelevant history objects (A_(HO)). However, in an alternativeembodiment, the ratios of the area of the bounding box (A_(BB)) to theareas of the relevant history objects (A_(HO)) may be otherwisecomputed, in which case step 1716 would not be needed.

At this point, the process proceeds to FIG. 15B where the historymanager 56 gets the list for the next output time band of the timewindow for the historical request (step 1718). The history manager 56then gets the next history object in the list for the output time band(step 1720). Next, the history manager 56 sets a relevancy weight forthe history object, where the relevancy weight is indicative of arelevancy of the history object to the bounding box (step 1722). Forinstance, a history object includes anonymized user profile data for acorresponding geographic area. If that geographic area is within orsignificantly overlaps the bounding box, then the history object willhave a high relevancy weight. However, if the geographic area onlyoverlaps the bounding box slightly, then the history object will have alow relevancy weight. In this embodiment, the relevancy weight for thehistory object is set to an approximate ratio of the area of thebounding box (A_(BB)) to an area of the history object (A_(HO)) computedbased on a difference between the quadtree depth of the history object(D_(HO)) and the equivalent quadtree depth of the bounding box (D_(EQ)).The quadtree depth of the history object (D_(HO)) is stored in thehistory object. More specifically, in one embodiment, the relevancyweight of the history object is set according to the following:

${{relevancy} = {\frac{A_{BB}}{A_{HO}} \cong \left( \frac{1}{4} \right)^{D_{HO} - D_{BB}}}},{for}$D_(HO) > D_(BB), and relevancy = 1, for D_(HO) ≤ D_(BB).

Next, the history manager 56 generates an aggregate profile for thehistory object using the user profile of the requesting user, which forthis example is the user 20-1, or a select subset thereof (step 1724).Note that the requesting user 20-1 may be enabled to select a subset ofhis user profile to be compared to the user profiles of the anonymoususer records in the history objects by, for example, selecting one ormore desired profile categories. In order to generate the aggregateprofile for the history object, the history manager 56 compares the userprofile of the user 20-1, or the select subset thereof, to the userprofiles of the anonymous user records stored in the history object. Theresulting aggregate profile for the history object includes a number ofuser matches and a total number of users. In the embodiment where userprofiles include lists of keywords for a number of profile categories,the number of user matches is the number of anonymous user records inthe history object having user profiles that include at least onekeyword that matches at least one keyword in the user profile of theuser 20-1 or at least one keyword in the select subset of the userprofile of the user 20-1. The total number of users is the total numberof anonymous user records in the history object. In addition oralternatively, the aggregate profile for the history object may includea list of keywords from the user profile of the user 20-1 or the selectsubset of the user profile of the user 20-1 having at least one usermatch. Still further, the aggregate profile for the history object mayinclude the number of user matches for each of the keywords from theuser profile of the user 20-1 or the select subset of the user profileof the user 20-1 having at least one user match.

The history manager 56 then determines whether there are more historyobjects in the list for the output time band (step 1726). If so, theprocess returns to step 1720 and is repeated until all of the historyobjects in the list for the output time band have been processed. Onceall of the history objects in the list for the output time band havebeen processed, the history manager 56 combines the aggregate profilesof the history objects in the output time band to provide a combinedaggregate profile for the output time band. More specifically, in thisembodiment, the history manager 56 computes a weighted average of theaggregate profiles for the history objects in the output time band usingthe relevancy weights of the history objects (step 1728). In oneembodiment, the aggregate profile of each of the history objectsincludes the number of user matches for the history object and the totalnumber of users for the history object. In this embodiment, the weightedaverage of the aggregate profiles of the history objects in the outputtime band (i.e., the average aggregate profile for the output time band)includes the weighted average of the number of user matches for all ofthe history objects in the output time band, which may be computed as:

${{user\_ matches}_{AVG} = \frac{\sum\limits_{i = 1}^{n}\; \left( {{{relevancy}_{i} \cdot {number\_ of}}{\_ user}{\_ matches}_{i}} \right)}{\sum\limits_{i = 1}^{n}\; {relevancy}_{i}}},$

where relevancy_(i) is the relevancy weight computed in step 1722 forthe i-th history object, number_of_user_matches_(i) is the number ofuser matches from the aggregate profile of the i-th history object, andn is the number of history objects in the list for the output time band.In a similar manner, in this embodiment, the average aggregate profilefor the output time band includes the weighted average of the totalnumber of users for all of the history objects in the output time band,which may be computed as:

${{total\_ users}_{AVG} = \frac{\sum\limits_{i = 1}^{n}\; \left( {{relevancy}_{i} \cdot {total\_ user}_{i}} \right)}{\sum\limits_{i = 1}^{n}\; {relevancy}_{i}}},$

where relevancy_(i) is the relevancy weight computed in step 1722 forthe i-th history object, total_users_(i) is the total number of usersfrom the aggregate profile of the i-th history object, and n is thenumber of history objects in the list for the output time band. Inaddition or alternatively, the average aggregate profile for the outputtime band may include the weighted average of the ratio of user matchesto total users for all of the history objects in the output time band,which may be computed as:

${\frac{user\_ matches}{{total\_ users}_{AVG}} = \frac{\sum\limits_{i = 1}^{n}\; \left( {{relevancy}_{i} \cdot \frac{{number\_ of}{\_ user}{\_ matches}_{i}}{{total\_ user}_{i}}} \right)}{\sum\limits_{i = 1}^{n}\; {relevancy}_{i}}},$

where relevancy_(i) is the relevancy weight computed in step 1722 forthe i-th history object, number_of_user_matches_(i) is the number ofuser matches from the aggregate profile of the i-th history object,total_users_(i) is the total number of users from the aggregate profileof the i-th history object, and n is the number of history objects inthe list for the output time band.

In addition or alternatively, if the aggregate profiles for the historyobjects in the output time band include the number of user matches foreach keyword in the user profile of the user 20-1, or the select subsetthereof, having at least one user match, the average aggregate profilefor the output time band may include a weighted average of the number ofuser matches for each of those keywords, which may be computed as:

${{user\_ matches}_{{KEYWORD\_ j},{AVG}} = \frac{\sum\limits_{i = 1}^{n}\; \left( {{{relevancy}_{i} \cdot {number\_ of}}{\_ user}{\_ matches}_{{KEYWORD\_ j},i}} \right)}{\sum\limits_{i = 1}^{n}\; {relevancy}_{i}}},$

where relevancy_(i) is the relevancy weight computed in step 1722 forthe i-th history object, number_of_user_matches_(KEYWORD) _(—) _(j,i) isthe number of user matches for the j-th keyword for the i-th historyobject, and n is the number of history objects in the list for theoutput time band. In addition or alternatively, the average aggregateprofile for the output time band may include the weighted average of theratio of the user matches to total users for each keyword, which may becomputed as:

${\frac{user\_ matches}{{total\_ users}_{{KEYWORD\_ j},{AVG}}} = \frac{\sum\limits_{i = 1}^{n}\; \left( {{relevancy}_{i} \cdot \frac{{number\_ of}{\_ user}{\_ matches}_{{KEYWORD\_ j},i}}{{total\_ user}_{i}}} \right)}{\sum\limits_{i = 1}^{n}\; {relevancy}_{i}}},$

where relevancy_(i) is the relevancy weight computed in step 1722 forthe i-th history object, number_of_user_matches_(KEYWORD) _(—) _(j,i) isthe number of user matches for the j-th keyword for the i-th historyobject, total_users_(i) is the total number of users from the aggregateprofile of the i-th history object, and n is the number of historyobjects in the list for the output time band.

Next, the history manager 56 determines whether there are more outputtime bands to process (step 1730). If so, the process returns to step1718 and is repeated until the lists for all output time bands have beenprocessed. Once all of the output time bands have been processed, thehistory manager 56 outputs the combined aggregate profiles for theoutput time bands. More specifically, in this embodiment, the historymanager 56 outputs the weighted average aggregate profiles computed instep 1728 for the output time bands as the historical aggregate profiledata to be returned to the mobile device 18-1 (step 1732).

FIG. 16 is an exemplary Graphical User Interface (GUI) 108 that may beprovided by the MAP application 32-1 of the mobile device 18-1 (FIG. 1)in order to present historical aggregate profile data in a time contextaccording to one embodiment of this disclosure. In operation, the MAPapplication 32-1 issues a historical request for a POI 110 in the mannerdescribed above. In response, the MAP server 12 uses the process ofFIGS. 15A and 15B to generate historical aggregate profile data inresponse to the historical request in the time context. Morespecifically, the historical aggregate profile data includes an averageaggregate profile for each of a number of output time bands within atime window established for the historical request. In this example, thetime window is a four week period extending from the week of July 5^(th)to the week of July 26^(th).

Using the average aggregate profiles for the output time bands includedin the historical aggregate profile data, the MAP application 32-1generates a timeline 112 for the time window of the historical request.The timeline 112 is a graphical illustration of the average aggregateprofiles for the output time bands. For example, if the averageaggregate profile for each of the output time bands includes a weightedaverage of the number of user matches and a weighted average of thenumber of total users for the output time band, the timeline 112 may beindicative of the ratio of the weighted average of user matches to theweighted average of total users for each of the output time bands. Inthis example, the output time bands having a ratio of weighted averageof user matches to weighted average of total users that is less than0.25 are represented as having a low similarity, the output time bandshaving a ratio of weighted average of user matches to weighted averageof total users that is in the range of 0.25-0.75 are represented ashaving varying degrees of intermediate similarity, and the output timebands having a ratio of weighted average of user matches to weightedaverage of total users that is greater than 0.75 are represented ashaving a high similarity. Note that output time bands for which thereare no history objects may be grayed-out or otherwise indicated.

In addition, in this example, the GUI 108 also includes a secondtimeline 114 that zooms in on an area of the timeline 112 that includesthe most activity or that includes the greatest number of output timebands having a high or medium similarity. Lastly, in this example, theGUI 108 includes an aggregate profile 116 for a crowd that is currentlyat the POI. Note that crowds and aggregate profiles for the crowds arediscussed below in detail.

FIGS. 17A and 17B illustrate a flow chart of a process for generatinghistorical aggregate profile data in a geographic context according toone embodiment of the present disclosure. First, upon receiving ahistorical request, the history manager 56 establishes a bounding boxfor the historical request based on the POI or the AOI for thehistorical request (step 1800). Note that while a bounding box is usedin this example, other geographic shapes may be used to define abounding region for the historical request (e.g., a bounding circle). Inthis embodiment, the historical request is from a mobile device of arequesting user, which in this example is the user 20-1. If thehistorical request is for a POI, the bounding box is a geographic regioncorresponding to or surrounding the POI. For example, the bounding boxmay be a square geographic region of a predefined size centered on thePOI. If the historical request is for an AOI, the bounding box is theAOI. In addition to establishing the bounding box, the history manager56 establishes a time window for the historical request (step 1802). Forexample, if the historical request is for the last week and the currentdate and time are Sep. 17, 2009 at 10:00 pm, the history manager 56 maygenerate the time window as Sep. 10, 2009 at 10:00 pm through Sep. 17,2009 at 10:00 pm.

Next, the history manager 56 obtains history objects relevant to thebounding box and the time window of the historical request from thedatastore 64 of the MAP server 12 (step 1804). The relevant historyobjects are history objects recorded for time periods within orintersecting the time window and for locations, or geographic areas,within or intersecting the bounding box for the historical request. Thehistory manager 56 then sorts the relevant history objects into basequadtree regions. More specifically, in this embodiment, the historymanager 56 creates an empty list for each relevant base quadtree region(step 1806). A relevant base quadtree region is a base quadtree regionwithin which all or at least a portion of the bounding box is located.Therefore, for example, if a bounding box is located at the intersectionof four base quadtree regions such that the bounding box overlaps aportion of each of the four base quadtree regions, then all four of thebounding boxes would be identified as relevant base quadtree regions. Incontrast, if the bounding box is contained within a single base quadtreeregion, then that base quadtree region is the only relevant basequadtree region.

The history manager 56 then gets the next history object from thehistory objects identified in step 1804 as being relevant to thehistorical request (step 1808) and adds that history object to the listfor the appropriate base quadtree region (step 1810). The historymanager 56 then determines whether there are more relevant historyobjects to sort (step 1812). If so, the process returns to step 1808 andis repeated until all of the relevant history objects have been sortedinto the appropriate base quadtree regions.

Once sorting is complete, the process proceeds to FIG. 17B. Thefollowing steps generally operate to divide each base quadtree regioninto a grid, where a size of each grid location is set to a smallesthistory record size of all the history objects sorted into the list forthat base quadtree region. Using the history objects in the basequadtree region, aggregate profiles are generated for each of the gridlocations covered by the history object. Then, a combined aggregateprofile is generated for each grid location based on the aggregateprofiles generated using the corresponding history objects.

More specifically, the history manager 56 gets the list for the nextbase quadtree region (step 1814). The history manager 56 then gets thenext history object in the list for the base quadtree region (step1816). Next, the history manager 56 creates an aggregate profile for thehistory object using the user profile of the requesting user, which inthis example is the user 20-1, or a select subset of the user profile ofthe requesting user (step 1818). Note that the user 20-1 may be enabledto select a subset of his user profile to be used for aggregate profilecreation by, for example, selecting one or more profile categories. Inorder to generate the aggregate profile for the history object, thehistory manager 56 compares the user profile of the user 20-1, or theselect subset thereof, to the user profiles of the anonymous userrecords stored in the history object. The resulting aggregate profilefor the history object includes a number of user matches and a totalnumber of users. In the embodiment where user profiles include lists ofkeywords for a number of profile categories, the number of user matchesis the number of anonymous user records in the history object havinguser profiles that include at least one keyword that matches at leastone keyword in the user profile of the user 20-1 or at least one keywordin the select subset of the user profile of the user 20-1. The totalnumber of users is the total number of anonymous user records in thehistory object.

Next, the history manager 56 determines whether a size of the historyobject is greater than the smallest history object size in the list ofhistory objects for the base quadtree region (step 1820). If not, theaggregate profile for the history object is added to an output list forthe corresponding grid location for the base quadtree region (step 1822)and the process proceeds to step 1830. If the size of the history objectis greater than the smallest history object size, the history manager 56splits the geographic area, or location, of the history object into anumber of grid locations each of the smallest history object size of allthe history objects in the list for the base quadtree region (step1824). The history manager 56 then divides the aggregate profile of thehistory object evenly over the grid locations for the history object(step 1826) and adds resulting aggregate profiles for the grid locationsto output lists for those grid locations (step 1828). For example, ifthe geographic area of the history object is split into four gridlocations and the aggregate profile for the history object includeseight user matches and sixteen total users, then the aggregate profileis divided evenly over the four grid locations such that each of thefour grid locations is given an aggregate profile of two user matchesand four total users.

The history manager 56 then determines whether there are more historyobjects to process for the base quadtree region (step 1830). If so, theprocess returns to step 1816 and is repeated until all of the historyobjects for the base quadtree region are processed. At that point, foreach grid location in the base quadtree region having at least oneaggregate profile in its output list, the history manager 56 combinesthe aggregate profiles in the output list for the grid location toprovide a combined aggregate profile for the grid location. Morespecifically, in this embodiment, the history manager 56 computesaverage aggregate profiles for the grid locations for the base quadtreeregion (step 1832). In one embodiment, for each grid location, theaverage aggregate profile for the grid location includes an averagenumber of user matches and an average total number of users for all ofthe aggregate profiles in the output list for that grid location.

Next, the history manager 56 determines whether there are more relevantbase quadtree regions to process (step 1834). If so, the process returnsto step 1814 and is repeated until all of the relevant base quadtreeregions have been processed. At that point, the history manager 56outputs the grid locations and the average aggregate profiles for thegrid locations in each of the relevant base quadtree regions (step1836). The grid locations and their corresponding average aggregateprofiles form the historical aggregate profile data that is returned tothe mobile device 18-1 of the user 20-1 in response to the historicalrequest.

FIG. 18 illustrates an exemplary GUI 118 that may be provided by the MAPapplication 32-1 of the mobile device 18-1 (FIG. 1) to presenthistorical aggregate profile data in the geographic context to the user20-1 in response to a historical request. As illustrated, the GUI 118includes a map 120 including a grid 122. The grid 122 provides graphicalinformation indicative of aggregate profiles for grid locations returnedby the MAP server 12 in response to a historical request. The GUI 118also includes buttons 124 and 126 enabling the user 20-1 to zoom in orzoom out on the map 120, buttons 128 and 130 enabling the user 20-1 totoggle between the traditional map view as shown or a satellite mapview, buttons 132 and 134 enabling the user 20-1 to switch betweenhistorical mode and a current mode (i.e., a view of current crowd dataas discussed below in detail), and buttons 136 and 138 enabling the user20-1 to hide or show POIs on the map 120.

It should be noted that while the aggregate profiles in FIGS. 15Athrough 18 are generated based on the user profile of the user 20-1 or aselect subset of the user profile of the user 20-1, the aggregateprofiles may alternatively be generated based on a target user profiledefined or otherwise specified by the user 20-1. For example, the user20-1 may define a target profile for a type of person with which theuser 20-1 would like to interact. Then, by making a historical requestwith the target profile, the user 20-1 can learn whether people matchingthe target profile are historically located at a POI or an AOI.

FIG. 19 illustrates the operation of the system 10 of FIG. 1 wherein thesubscriber device 22 is enabled to request and receive historicalaggregate profile data from the MAP server 12 according to oneembodiment of the present disclosure. Note that, in a similar manner,the third-party service 26 may send historical requests to the MAPserver 12. As illustrated, in this embodiment, the subscriber device 22sends a historical request to the MAP server 12 (step 1900). Thesubscriber device 22 sends the historical request to the MAP server 12via the web browser 38. In one embodiment, the historical requestidentifies either a POI or an AOI and a time window. The historicalrequest may be made in response to user input from the subscriber 24 ofthe subscriber device 22 or made automatically in response to an eventsuch as, for example, navigation to a website associated with a POI(e.g., navigation to a website of a restaurant).

Upon receiving the historical request, the MAP server 12 processes thehistorical request (step 1902). More specifically, as discussed above,the historical request is processed by the history manager 56 of the MAPserver 12. First, the history manager 56 obtains history objects thatare relevant to the historical request from the datastore 64 of the MAPserver 12. The relevant history objects are those relevant to the POI orthe AOI and the time window for the historical request. The historymanager 56 then processes the relevant history objects to providehistorical aggregate profile data for the POI or the AOI in a timecontext and/or a geographic context. In this embodiment, the historicalaggregate profile data is based on comparisons of the user profiles ofthe anonymous user records in the relevant history objects to oneanother. In another embodiment, the aggregate profile data is based oncomparisons of the user profiles of the anonymous user records in therelevant history objects and a target user profile.

Once the MAP server 12 has processed the historical request, the MAPserver 12 returns the resulting historical aggregate profile data to thesubscriber device 22 (step 1904). The historical aggregate profile datamay be in the time context or the geographic context. In this embodimentwhere the historical aggregate profile data is to be presented via theweb browser 38 of the subscriber device 22, the MAP server 12 formatsthe historical aggregate profile data in a suitable format beforesending the historical aggregate profile data to the web browser 38 ofthe subscriber device 22. Upon receiving the historical aggregateprofile data, the web browser 38 of the subscriber device 22 presentsthe historical aggregate profile data to the user 20-1 (step 1906).

FIGS. 20A and 20B illustrate a process for generating historicalaggregate profile data in a time context in response to a historicalrequest from the subscriber 24 at the subscriber device 22 according toone embodiment of the present disclosure. The process of FIGS. 20A and20B is substantially the same as that described above with respect toFIGS. 15A and 15B. More specifically, steps 2000 through 2022 aresubstantially the same as steps 1700 through 1722 of FIGS. 15A and 15B.Likewise, steps 2026 through 2032 are substantially the same as steps1726 through 1732 of FIG. 15B. However, step 2024 of FIG. 20B isdifferent from step 1724 of FIG. 15B with respect to the manner in whichthe aggregate profiles for the relevant history objects are computed.

More specifically, in this embodiment, since the historical request isfrom the subscriber 24, the aggregate profile for the history object isgenerated by comparing the user profiles of the anonymous user recordsin the history object to one another. In this embodiment, the aggregateprofile for the history object includes an aggregate list of keywordsfrom the user profiles of the anonymous user records, the number ofoccurrences of each of those keywords in the user profiles of theanonymous user records, and the total number of anonymous user recordsin the history object. As such, in step 2028, the weighted average ofthe aggregate profiles for the history objects in the output time bandmay provide an average aggregate profile including, for each keywordoccurring in the aggregate profile of at least one of the historyobjects, a weighted average of the number of occurrences of the keyword.In addition, the average aggregate profile may include a weightedaverage of the total number of anonymous user records in the historyobjects. In addition or alternatively, the average aggregate profile mayinclude, for each keyword, a weighted average of the number ofoccurrences of the keyword to the total number of anonymous userrecords.

FIGS. 21A and 21B illustrate a process for generating historicalaggregate profile data in a geographic context in response to ahistorical request from the subscriber 24 at the subscriber device 22according to one embodiment of the present disclosure. The process ofFIGS. 21A and 21B is substantially the same as that described above withrespect to FIGS. 17A and 17B. More specifically, steps 2100 through 2116and 2120 through 2136 are substantially the same as steps 1800 through1816 and 1820 through 1836 of FIGS. 17A and 17B. However, step 2118 ofFIG. 21B is different from step 1818 of FIG. 17B with respect to themanner in which the aggregate profiles for the history objects arecomputed.

More specifically, in this embodiment, since the historical request isfrom the subscriber 24, the aggregate profile for the history object isgenerated by comparing the user profiles of the anonymous user recordsin the history object to one another. In this embodiment, the aggregateprofile for the history object includes an aggregate list of keywordsfrom the user profiles of the anonymous user records, the number ofoccurrences of each of those keywords in the user profiles of theanonymous user records, and the total number of anonymous user recordsin the history object. As such, in step 2132, the weighted average ofthe aggregate profiles for the each of the grid locations may provide anaverage aggregate profile including, for each keyword, a weightedaverage of the number of occurrences of the keyword. In addition, theaverage aggregate profile for each grid location may include a weightedaverage of the total number of anonymous user records. In addition oralternatively, the average aggregate profile for each grid location mayinclude, for each keyword, a weighted average of the number ofoccurrences of the keyword to the total number of anonymous userrecords.

FIG. 22 begins a discussion of the operation of the crowd analyzer 58 toform crowds of users according to one embodiment of the presentdisclosure. Specifically, FIG. 22 is a flow chart for a spatial crowdformation process according to one embodiment of the present disclosure.Note that, in one embodiment, this process is performed in response to arequest for crowd data for a POI or an AOI. In another embodiment, thisprocess may be performed proactively by the crowd analyzer 58 as, forexample, a background process.

First, the crowd analyzer 58 establishes a bounding box for the crowdformation process (step 2200). Note that while a bounding box is used inthis example, other geographic shapes may be used to define a boundingregion for the crowd formation process (e.g., a bounding circle). In oneembodiment, if crowd formation is performed in response to a specificrequest, the bounding box is established based on the POI or the AOI ofthe request. If the request is for a POI, then the bounding box is ageographic area of a predetermined size centered at the POI. If therequest is for an AOI, the bounding box is the AOI. Alternatively, ifthe crowd formation process is performed proactively, the bounding boxis a bounding box of a predefined size.

The crowd analyzer 58 then creates a crowd for each individual user inthe bounding box (step 2202). More specifically, the crowd analyzer 58queries the datastore 64 of the MAP server 12 to identify userscurrently located within the bounding box. Then, a crowd of one user iscreated for each user currently located within the bounding box. Next,the crowd analyzer 58 determines the two closest crowds in the boundingbox (step 2204) and determines a distance between the two crowds (step2206). The distance between the two crowds is a distance between crowdcenters of the two crowds. Note that the crowd center of a crowd of oneis the current location of the user in the crowd. The crowd analyzer 58then determines whether the distance between the two crowds is less thanan optimal inclusion distance (step 2208). In this embodiment, theoptimal inclusion distance is a predefined static distance. If thedistance between the two crowds is less than the optimal inclusiondistance, the crowd analyzer 58 combines the two crowds (step 2210) andcomputes a new crowd center for the resulting crowd (step 2212). Thecrowd center may be computed based on the current locations of the usersin the crowd using a center of mass algorithm. At this point the processreturns to step 2204 and is repeated until the distance between the twoclosest crowds is not less than the optimal inclusion distance. At thatpoint, the crowd analyzer 58 discards any crowds with less than threeusers (step 2214). Note that throughout this disclosure crowds are onlymaintained if the crowds include three or more users. However, whilethree users is the preferred minimum number of users in a crowd, thepresent disclosure is not limited thereto. The minimum number of usersin a crowd may be defined as any number greater than or equal to twousers.

FIGS. 23A through 23D graphically illustrate the crowd formation processof FIG. 22 for an exemplary bounding box 139. In FIGS. 23A through 23D,crowds are noted by dashed circles, and the crowd centers are noted bycross-hairs (+). As illustrated in FIG. 23A, initially, the crowdanalyzer 58 creates crowds 140 through 148 for the users in thegeographic area, where, at this point, each of the crowds 140 through148 includes one user. The current locations of the users are the crowdcenters of the crowds 140 through 148. Next, the crowd analyzer 58determines the two closest crowds and a distance between the two closestcrowds. In this example, at this point, the two closest crowds arecrowds 142 and 144, and the distance between the two closest crowds 142and 144 is less than the optimal inclusion distance. As such, the twoclosest crowds 142 and 144 are combined by merging crowd 144 into crowd142, and a new crowd center (+) is computed for the crowd 142, asillustrated in FIG. 23B. Next, the crowd analyzer 58 again determinesthe two closest crowds, which are now crowds 140 and 142. The crowdanalyzer 58 then determines a distance between the crowds 140 and 142.Since the distance is less than the optimal inclusion distance, thecrowd analyzer 58 combines the two crowds 140 and 142 by merging thecrowd 140 into the crowd 142, and a new crowd center (+) is computed forthe crowd 142, as illustrated in FIG. 23C. At this point, there are nomore crowds separated by less than the optimal inclusion distance. Assuch, the crowd analyzer 58 discards crowds having less than threeusers, which in this example are crowds 146 and 148. As a result, at theend of the crowd formation process, the crowd 142 has been formed withthree users, as illustrated in FIG. 23D.

FIGS. 24A through 24D illustrate a flow chart for a spatial crowdformation process according to another embodiment of the presentdisclosure. In this embodiment, the spatial crowd formation process istriggered in response to receiving a location update for one of theusers 20-1 through 20-N and is preferably repeated for each locationupdate received for the users 20-1 through 20-N. As such, first, thecrowd analyzer 58 receives a location update, or a new location, for auser (step 2300). Assume that, for this example, the location update isreceived for the user 20-1. In response, the crowd analyzer 58 retrievesan old location of the user 20-1, if any (step 2302). The old locationis the current location of the user 20-1 prior to receiving the newlocation. The crowd analyzer 58 then creates a new bounding box of apredetermined size centered at the new location of the user 20-1 (step2304) and an old bounding box of a predetermined size centered at theold location of the user 20-1, if any (step 2306). The predeterminedsize of the new and old bounding boxes may be any desired size. As oneexample, the predetermined size of the new and old bounding boxes is 40meters by 40 meters. Note that if the user 20-1 does not have an oldlocation (i.e., the location received in step 2300 is the first locationreceived for the user 20-1), then the old bounding box is essentiallynull. Also note that while bounding “boxes” are used in this example,the bounding areas may be of any desired shape.

Next, the crowd analyzer 58 determines whether the new and old boundingboxes overlap (step 2308). If so, the crowd analyzer 58 creates abounding box encompassing the new and old bounding boxes (step 2310).For example, if the new and old bounding boxes are 40×40 meter regionsand a 1×1 meter square at the northeast corner of the new bounding boxoverlaps a 1×1 meter square at the southwest corner of the old boundingbox, the crowd analyzer 58 may create a 79×79 meter square bounding boxencompassing both the new and old bounding boxes.

The crowd analyzer 58 then determines the individual users and crowdsrelevant to the bounding box created in step 2310 (step 2312). Thecrowds relevant to the bounding box are crowds that are within oroverlap the bounding box (e.g., have at least one user located withinthe bounding box). The individual users relevant to the bounding box areusers that are currently located within the bounding box and not alreadypart of a crowd. Next, the crowd analyzer 58 computes an optimalinclusion distance for individual users based on user density within thebounding box (step 2314). More specifically, in one embodiment, theoptimal inclusion distance for individuals, which is also referred toherein as an initial optimal inclusion distance, is set according to thefollowing equation:

${{{initial\_ optimal}{\_ inclusion}{\_ dist}} = {a \cdot \sqrt{\frac{A_{BoundingBox}}{{number\_ of}{\_ users}}}}},$

where a is a number between 0 and 1, A_(BoundingBox) is an area of thebounding box, and number_of_users is the total number of users in thebounding box. The total number of users in the bounding box includesboth individual users that are not already in a crowd and users that arealready in a crowd. In one embodiment, a is ⅔.

The crowd analyzer 58 then creates a crowd for each individual userwithin the bounding box that is not already included in a crowd and setsthe optimal inclusion distance for the crowds to the initial optimalinclusion distance (step 2316). At this point, the process proceeds toFIG. 24B where the crowd analyzer 58 analyzes the crowds relevant to thebounding box to determine whether any of the crowd members (i.e., usersin the crowds) violate the optimal inclusion distance of their crowds(step 2318). Any crowd member that violates the optimal inclusiondistance of his or her crowd is then removed from that crowd (step2320). The crowd analyzer 58 then creates a crowd of one user for eachof the users removed from their crowds in step 2320 and sets the optimalinclusion distance for the newly created crowds to the initial optimalinclusion distance (step 2322).

Next, the crowd analyzer 58 determines the two closest crowds for thebounding box (step 2324) and a distance between the two closest crowds(step 2326). The distance between the two closest crowds is the distancebetween the crowd centers of the two closest crowds. The crowd analyzer58 then determines whether the distance between the two closest crowdsis less than the optimal inclusion distance of a larger of the twoclosest crowds (step 2328). If the two closest crowds are of the samesize (i.e., have the same number of users), then the optimal inclusiondistance of either of the two closest crowds may be used. Alternatively,if the two closest crowds are of the same size, the optimal inclusiondistances of both of the two closest crowds may be used such that thecrowd analyzer 58 determines whether the distance between the twoclosest crowds is less than the optimal inclusion distances of both ofthe two closest crowds. As another alternative, if the two closestcrowds are of the same size, the crowd analyzer 58 may compare thedistance between the two closest crowds to an average of the optimalinclusion distances of the two closest crowds.

If the distance between the two closest crowds is less than the optimalinclusion distance, the two closest crowds are combined or merged (step2330), and a new crowd center for the resulting crowd is computed (step2332). Again, a center of mass algorithm may be used to compute thecrowd center of a crowd. In addition, a new optimal inclusion distancefor the resulting crowd is computed (step 2334). In one embodiment, thenew optimal inclusion distance for the resulting crowd is computed as:

${{average} = {\frac{1}{n + 1} \cdot \left( {{{initial\_ optimal}{\_ inclusion}{\_ dist}} + {\sum\limits_{i = 1}^{n}\; d_{i}}} \right)}},{{{optimial\_ inclusion}{\_ dist}} = {{average} + \sqrt{\left( {\frac{1}{n} \cdot {\sum\limits_{i = 1}^{n}\; \left( {d_{i} - {average}} \right)^{2}}} \right)}}},$

where n is the number of users in the crowd and is a distance betweenthe ith user and the crowd center. In other words, the new optimalinclusion distance is computed as the average of the initial optimalinclusion distance and the distances between the users in the crowd andthe crowd center plus one standard deviation.

At this point, the crowd analyzer 58 determines whether a maximum numberof iterations have been performed (step 2336). The maximum number ofiterations is a predefined number that ensures that the crowd formationprocess does not indefinitely loop over steps 2318 through 2334 or loopover steps 2318 through 2334 more than a desired maximum number oftimes. If the maximum number of iterations has not been reached, theprocess returns to step 2318 and is repeated until either the distancebetween the two closest crowds is not less than the optimal inclusiondistance of the larger crowd or the maximum number of iterations hasbeen reached. At that point, the crowd analyzer 58 discards crowds withless than three users, or members (step 2338) and the process ends.

Returning to step 2308 in FIG. 24A, if the new and old bounding boxes donot overlap, the process proceeds to FIG. 24C and the bounding box to beprocessed is set to the old bounding box (step 2340). In general, thecrowd analyzer 58 then processes the old bounding box in much the samemanner as described above with respect to steps 2312 through 2338. Morespecifically, the crowd analyzer 58 determines the individual users andcrowds relevant to the bounding box (step 2342). The crowds relevant tothe bounding box are crowds that are within or overlap the bounding box(e.g., have at least one user located within the bounding box). Theindividual users relevant to the bounding box are users that arecurrently located within the bounding box and not already part of acrowd. Next, the crowd analyzer 58 computes an optimal inclusiondistance for individual users based on user density within the boundingbox (step 2344). More specifically, in one embodiment, the optimalinclusion distance for individuals, which is also referred to herein asan initial optimal inclusion distance, is set according to the followingequation:

${{{initial\_ optimal}{\_ inclusion}{\_ dist}} = {a \cdot \sqrt{\frac{A_{BoundingBox}}{{number\_ of}{\_ users}}}}},$

where a is a number between 0 and 1, A_(BoundingBox) is an area of thebounding box, and number_of_users is the total number of users in thebounding box. The total number of users in the bounding box includesboth individual users that are not already in a crowd and users that arealready in a crowd. In one embodiment, a is ⅔.

The crowd analyzer 58 then creates a crowd of one user for eachindividual user within the bounding box that is not already included ina crowd and sets the optimal inclusion distance for the crowds to theinitial optimal inclusion distance (step 2346). At this point, the crowdanalyzer 58 analyzes the crowds for the bounding box to determinewhether any crowd members (i.e., users in the crowds) violate theoptimal inclusion distance of their crowds (step 2348). Any crowd memberthat violates the optimal inclusion distance of his or her crowd is thenremoved from that crowd (step 2350). The crowd analyzer 58 then createsa crowd of one user for each of the users removed from their crowds instep 2350 and sets the optimal inclusion distance for the newly createdcrowds to the initial optimal inclusion distance (step 2352).

Next, the crowd analyzer 58 determines the two closest crowds in thebounding box (step 2354) and a distance between the two closest crowds(step 2356). The distance between the two closest crowds is the distancebetween the crowd centers of the two closest crowds. The crowd analyzer58 then determines whether the distance between the two closest crowdsis less than the optimal inclusion distance of a larger of the twoclosest crowds (step 2358). If the two closest crowds are of the samesize (i.e., have the same number of users), then the optimal inclusiondistance of either of the two closest crowds may be used. Alternatively,if the two closest crowds are of the same size, the optimal inclusiondistances of both of the two closest crowds may be used such that thecrowd analyzer 58 determines whether the distance between the twoclosest crowds is less than the optimal inclusion distances of both ofthe two closest crowds. As another alternative, if the two closestcrowds are of the same size, the crowd analyzer 58 may compare thedistance between the two closest crowds to an average of the optimalinclusion distances of the two closest crowds.

If the distance between the two closest crowds is less than the optimalinclusion distance, the two closest crowds are combined or merged (step2360), and a new crowd center for the resulting crowd is computed (step2362). Again, a center of mass algorithm may be used to compute thecrowd center of a crowd. In addition, a new optimal inclusion distancefor the resulting crowd is computed (step 2364). As discussed above, inone embodiment, the new optimal inclusion distance for the resultingcrowd is computed as:

${{average} = {\frac{1}{n + 1} \cdot \left( {{{initial\_ optimal}{\_ inclusion}{\_ dist}} + {\sum\limits_{i = 1}^{n}\; d_{i}}} \right)}},{{{optimial\_ inclusion}{\_ dist}} = {{average} + \sqrt{\left( {\frac{1}{n} \cdot {\sum\limits_{i = 1}^{n}\; \left( {d_{i} - {average}} \right)^{2}}} \right)}}},$

where n is the number of users in the crowd and is a distance betweenthe ith user and the crowd center. In other words, the new optimalinclusion distance is computed as the average of the initial optimalinclusion distance and the distances between the users in the crowd andthe crowd center plus one standard deviation.

At this point, the crowd analyzer 58 determines whether a maximum numberof iterations have been performed (step 2366). If the maximum number ofiterations has not been reached, the process returns to step 2348 and isrepeated until either the distance between the two closest crowds is notless than the optimal inclusion distance of the larger crowd or themaximum number of iterations has been reached. At that point, the crowdanalyzer 58 discards crowds with less than three users, or members (step2368). The crowd analyzer 58 then determines whether the crowd formationprocess for the new and old bounding boxes is done (step 2370). In otherwords, the crowd analyzer 58 determines whether both the new and oldbounding boxes have been processed. If not, the bounding box is set tothe new bounding box (step 2372), and the process returns to step 2342and is repeated for the new bounding box. Once both the new and oldbounding box have been processed, the crowd formation process ends.

FIGS. 25A through 25D graphically illustrate the crowd formation processof FIGS. 24A through 24D for a scenario where the crowd formationprocess is triggered by a location update for a user having no oldlocation. In this scenario, the crowd analyzer 58 creates a new boundingbox 150 for the new location of the user, and the new bounding box 150is set as the bounding box to be processed for crowd formation. Then, asillustrated in FIG. 25A, the crowd analyzer 58 identifies all individualusers currently located within the bounding box 150 and all crowdslocated within or overlapping the bounding box. In this example, crowd152 is an existing crowd relevant to the bounding box 150. Crowds areindicated by dashed circles, crowd centers are indicated by cross-hairs(+), and users are indicated as dots. Next, as illustrated in FIG. 25B,the crowd analyzer 58 creates crowds 154 through 158 of one user for theindividual users, and the optional inclusion distances of the crowds 154through 158 are set to the initial optimal inclusion distance. Asdiscussed above, the initial optimal inclusion distance is computed bythe crowd analyzer 58 based on a density of users within the boundingbox 150.

The crowd analyzer 58 then identifies the two closest crowds 154 and 156in the bounding box 150 and determines a distance between the twoclosest crowds 154 and 156. In this example, the distance between thetwo closest crowds 154 and 156 is less than the optimal inclusiondistance. As such, the two closest crowds 154 and 156 are merged and anew crowd center and new optimal inclusion distance are computed, asillustrated in FIG. 25C. The crowd analyzer 58 then repeats the processsuch that the two closest crowds 154 and 158 in the bounding box 150 areagain merged, as illustrated in FIG. 23D. At this point, the distancebetween the two closest crowds 152 and 154 is greater than theappropriate optimal inclusion distance. As such, the crowd formationprocess is complete.

FIGS. 26A through 26F graphically illustrate the crowd formation processof FIGS. 24A through 24D for a scenario where the new and old boundingboxes overlap. As illustrated in FIG. 26A, a user moves from an oldlocation to a new location, as indicated by an arrow. The crowd analyzer58 receives a location update for the user giving the new location ofthe user. In response, the crowd analyzer 58 creates an old bounding box160 for the old location of the user and a new bounding box 162 for thenew location of the user. Crowd 164 exists in the old bounding box 160,and crowd 166 exists in the new bounding box 162.

Since the old bounding box 160 and the new bounding box 162 overlap, thecrowd analyzer 58 creates a bounding box 168 that encompasses both theold bounding box 160 and the new bounding box 162, as illustrated inFIG. 26B. In addition, the crowd analyzer 58 creates crowds 170 through176 for individual users currently located within the bounding box 168.The optimal inclusion distances of the crowds 170 through 176 are set tothe initial optimal inclusion distance computed by the crowd analyzer 58based on the density of users in the bounding box 168.

Next, the crowd analyzer 58 analyzes the crowds 164, 166, and 170through 176 to determine whether any members of the crowds 164, 166, and170 through 176 violate the optimal inclusion distances of the crowds164, 166, and 170 through 176. In this example, as a result of the userleaving the crowd 164 and moving to his new location, both of theremaining members of the crowd 164 violate the optimal inclusiondistance of the crowd 164. As such, the crowd analyzer 58 removes theremaining users from the crowd 164 and creates crowds 178 and 180 of oneuser each for those users, as illustrated in FIG. 26C.

The crowd analyzer 58 then identifies the two closest crowds in thebounding box 168, which in this example are the crowds 174 and 176.Next, the crowd analyzer 58 computes a distance between the two crowds174 and 176. In this example, the distance between the two crowds 174and 176 is less than the initial optimal inclusion distance and, assuch, the two crowds 174 and 176 are combined. In this example, crowdsare combined by merging the smaller crowd into the larger crowd. Sincethe two crowds 174 and 176 are of the same size, the crowd analyzer 58merges the crowd 176 into the crowd 174, as illustrated in FIG. 26D. Anew crowd center and new optimal inclusion distance are then computedfor the crowd 174.

At this point, the crowd analyzer 58 repeats the process and determinesthat the crowds 166 and 172 are now the two closest crowds. In thisexample, the distance between the two crowds 166 and 172 is less thanthe optimal inclusion distance of the larger of the two crowds 166 and172, which is the crowd 166. As such, the crowd 172 is merged into thecrowd 166 and a new crowd center and optimal inclusion distance arecomputed for the crowd 166, as illustrated in FIG. 26E. At this point,there are no two crowds closer than the optimal inclusion distance ofthe larger of the two crowds. As such, the crowd analyzer 58 discardsany crowds having less than three members, as illustrated in FIG. 26F.In this example, the crowds 170, 174, 178, and 180 have less than threemembers and are therefore removed. The crowd 166 has three or moremembers and, as such, is not removed. At this point, the crowd formationprocess is complete.

FIGS. 27A through 27E graphically illustrate the crowd formation processof FIGS. 24A through 24D in a scenario where the new and old boundingboxes do not overlap. As illustrated in FIG. 27A, in this example, theuser moves from an old location to a new location. The crowd analyzer 58creates an old bounding box 182 for the old location of the user and anew bounding box 184 for the new location of the user. Crowds 186 and188 exist in the old bounding box 182, and crowd 190 exists in the newbounding box 184. In this example, since the old and new bounding boxes182 and 184 do not overlap, the crowd analyzer 58 processes the old andnew bounding boxes 182 and 184 separately.

More specifically, as illustrated in FIG. 27B, as a result of themovement of the user from the old location to the new location, theremaining users in the crowd 186 no longer satisfy the optimal inclusiondistance for the crowd 186. As such, the remaining users in the crowd186 are removed from the crowd 186, and crowds 192 and 194 of one usereach are created for the removed users as shown in FIG. 26C. In thisexample, no two crowds in the old bounding box 182 are close enough tobe combined. As such, processing of the old bounding box 182 iscomplete, and the crowd analyzer 58 proceeds to process the new boundingbox 184.

As illustrated in FIG. 27D, processing of the new bounding box 184begins by the crowd analyzer 58 creating a crowd 196 of one user for theuser. The crowd analyzer 58 then identifies the crowds 190 and 196 asthe two closest crowds in the new bounding box 184 and determines adistance between the two crowds 190 and 196. In this example, thedistance between the two crowds 190 and 196 is less than the optimalinclusion distance of the larger crowd, which is the crowd 190. As such,the crowd analyzer 58 combines the crowds 190 and 196 by merging thecrowd 196 into the crowd 190, as illustrated in FIG. 27E. A new crowdcenter and new optimal inclusion distance are then computed for thecrowd 190. At this point, the crowd formation process is complete.

Before proceeding, a variation of the spatial formation processdiscussed above with respect to FIGS. 24A through 24D, 25A through 25D,26A through 26F, and 27A through 27E will be described. In thisalternative embodiment, a location accuracy of the location update fromthe user received in step 2300 is considered. More specifically, in step2300, the location update received by the MAP server 12 includes theupdated location of the user 20-1 as well as a location accuracy for thelocation of the user 20-1, which may be expressed as, for example, aradius in meters from the location of the user 20-1. In the embodimentwhere the location of the user 20-1 is obtained from a GPS receiver ofthe mobile device 18-1, the location accuracy of the location of theuser 20-1 may be provided by the GPS receiver or derived from data fromthe GPS receiver as well be appreciated by one having ordinary skill inthe art.

Then, in steps 2302 and 2304, sizes of the new and old bounding boxescentered at the new and old locations of the user 20-1 are set as afunction of the location accuracy of the new and old locations of theuser 20-1. If the new location of the user 20-1 is inaccurate, then thenew bounding box will be large. If the new location of the user 20-1 isaccurate, then the new bounding box will be small. For example, thelength and width of the new bounding box may be set to M times thelocation accuracy of the new location of the user 20-1, where thelocation accuracy is expressed as a radius in meters from the newlocation of the user 20-1. The number M may be any desired number. Forexample, the number M may be 5. In a similar manner, the locationaccuracy of the old location of the user 20-1 may be used to set thelength and width of the old bounding box.

In addition, the location accuracy may be considered when computing theinitial optimal inclusion distances used for crowds of one user in steps2314 and 2344. As discussed above, the initial optimal inclusiondistance is computed based on the following equation:

${{{initial\_ optimal}{\_ inclusion}{\_ dist}} = {a \cdot \sqrt{\frac{A_{BoundingBox}}{{number\_ of}{\_ users}}}}},$

where a is a number between 0 and 1, A_(BoundingBox) is an area of thebounding box, and number_of_users is the total number of users in thebounding box. The total number of users in the bounding box includesboth individual users that are not already in a crowd and users that arealready in a crowd. In one embodiment, a is ⅔. However, if the computedinitial optimal inclusion distance is less than the location accuracy ofthe current location of the individual user in a crowd, then thelocation accuracy, rather than the computed value, is used for theinitial optimal inclusion distance for that crowd. As such, as locationaccuracy decreases, crowds become larger and more inclusive. Incontrast, as location accuracy increases, crowds become smaller and lessinclusive. In other words, the granularity with which crowds are formedis a function of the location accuracy.

Likewise, when new optimal inclusion distances for crowds are recomputedin steps 2334 and 2364, location accuracy may also be considered. Asdiscussed above, the new optimal inclusion distance may first becomputed based on the following equation:

${{average} = {\frac{1}{n + 1} \cdot \left( {{{initial\_ optimal}{\_ inclusion}{\_ dist}} + {\sum\limits_{i = 1}^{n}\; d_{i}}} \right)}},{{{optimial\_ inclusion}{\_ dist}} = {{average} + \sqrt{\left( {\frac{1}{n} \cdot {\sum\limits_{i = 1}^{n}\; \left( {d_{i} - {average}} \right)^{2}}} \right)}}},$

where n is the number of users in the crowd and d_(i) is a distancebetween the ith user and the crowd center. In other words, the newoptimal inclusion distance is computed as the average of the initialoptimal inclusion distance and the distances between the users in thecrowd and the crowd center plus one standard deviation. However, if thecomputed value for the new optimal inclusion distance is less than anaverage location accuracy of the users in the crowd, the averagelocation accuracy of the users in the crowd, rather than the computedvalue, is used as the new optimal inclusion distance.

FIG. 28 illustrates the operation the system 10 of FIG. 1 to enable themobile devices 18-1 through 18-N to request crowd data for currentlyformed crowds according to one embodiment of the present disclosure.Note that while in this example the request is initiated by the MAPapplication 32-1 of the mobile device 18-1, this discussion is equallyapplicable to the MAP applications 32-2 through 32-N of the other mobiledevices 18-2 through 18-N. In addition, in a similar manner, requestsmay be received from the third-party applications 34-1 through 34-N.

First, the MAP application 32-1 sends a crowd request to the MAP client30-1 (step 2400). The crowd request is a request for crowd data forcrowds currently formed near a specified POI or within a specified AOI.The crowd request may be initiated by the user 20-1 of the mobile device18-1 via the MAP application 32-1 or may be initiated automatically bythe MAP application 32-1 in response to an event such as, for example,start-up of the MAP application 32-1, movement of the user 20-1, or thelike. In one embodiment, the crowd request is for a POI, where the POIis a POI corresponding to the current location of the user 20-1, a POIselected from a list of POIs defined by the user 20-1, a POI selectedfrom a list of POIs defined by the MAP application 32-1 or the MAPserver 12, a POI selected by the user 20-1 from a map, a POI implicitlydefined via a separate application (e.g., POI is implicitly defined asthe location of the nearest Starbucks coffee house in response to theuser 20-1 performing a Google search for “Starbucks”), or the like. Ifthe POI is selected from a list of POIs, the list of POIs may includestatic POIs which may be defined by street addresses or latitude andlongitude coordinates, dynamic POIs which may be defined as the currentlocations of one or more friends of the user 20-1, or both. Note that insome embodiments, the user 20-1 may be enabled to define a POI byselecting a crowd center of a crowd as a POI, where the POI wouldthereafter remain static at that point and would not follow the crowd.

In another embodiment, the crowd request is for an AOI, where the AOImay be an AOI of a predefined shape and size centered at the currentlocation of the user 20-1, an AOI selected from a list of AOIs definedby the user 20-1, an AOI selected from a list of AOIs defined by the MAPapplication 32-1 or the MAP server 12, an AOI selected by the user 20-1from a map, an AOI implicitly defined via a separate application (e.g.,AOI is implicitly defined as an area of a predefined shape and sizecentered at the location of the nearest Starbucks coffee house inresponse to the user 20-1 performing a Google search for “Starbucks”),or the like. If the AOI is selected from a list of AOIs, the list ofAOIs may include static AOIs, dynamic AOIs which may be defined as areasof a predefined shape and size centered at the current locations of oneor more friends of the user 20-1, or both. Note that in someembodiments, the user 20-1 may be enabled to define an AOI by selectinga crowd such that an AOI is created of a predefined shape and sizecentered at the crowd center of the selected crowd. The AOI wouldthereafter remain static and would not follow the crowd. The POI or theAOI of the crowd request may be selected by the user 20-1 via the MAPapplication 32-1. In yet another embodiment, the MAP application 32-1automatically uses the current location of the user 20-1 as the POI oras a center point for an AOI of a predefined shape and size.

Upon receiving the crowd request, the MAP client 30-1 forwards the crowdrequest to the MAP server 12 (step 2402). Note that in some embodiments,the MAP client 30-1 may process the crowd request before forwarding thecrowd request to the MAP server 12. For example, in some embodiments,the crowd request may include more than one POI or more than one AOI. Assuch, the MAP client 30-1 may generate a separate crowd request for eachPOI or each AOI.

In response to receiving the crowd request from the MAP client 30-1, theMAP server 12 identifies one or more crowds relevant to the crowdrequest (step 2404). More specifically, in one embodiment, the crowdanalyzer 58 performs a crowd formation process such as that describedabove in FIG. 22 to form one or more crowds relevant to the POI or theAOI of the crowd request. In another embodiment, the crowd analyzer 58proactively forms crowds using a process such as that described above inFIGS. 24A through 24D and stores corresponding crowd records in thedatastore 64 of the MAP server 12. Then, rather than forming therelevant crowds in response to the crowd request, the crowd analyzer 58queries the datastore 64 to identify the crowds that are relevant to thecrowd request. The crowds relevant to the crowd request may be thosecrowds within or intersecting a bounding region, such as a bounding box,for the crowd request. If the crowd request is for a POI, the boundingregion is a geographic region of a predefined shape and size centered atthe POI. If the crowd request is for an AOI, the bounding region is theAOI.

Once the crowd analyzer 58 has identified the crowds relevant to thecrowd request, the MAP server 12 generates crowd data for the identifiedcrowds (step 2406). As discussed below in detail, the crowd data for theidentified crowds may include aggregate profiles for the crowds,information characterizing the crowds, or both. In addition, the crowddata may include spatial information defining the locations of thecrowds, the number of users in the crowds, the amount of time the crowdshave been located at or near the POI or within the AOI of the crowdrequest, or the like. The MAP server 12 then returns the crowd data tothe MAP client 30-1 (step 2408).

Upon receiving the crowd data, the MAP client 30-1 forwards the crowddata to the MAP application 32-1 (step 2410). Note that in someembodiments the MAP client 30-1 may process the crowd data beforesending the crowd data to the MAP application 32-1. The MAP application32-1 then presents the crowd data to the user 20-1 (step 2412). Themanner in which the crowd data is presented depends on the particularimplementation of the MAP application 32-1. In one embodiment, the crowddata is overlaid upon a map. For example, the crowds may be representedby corresponding indicators overlaid on a map. The user 20-1 may thenselect a crowd in order to view additional crowd data regarding thatcrowd such as, for example, the aggregate profile of that crowd,characteristics of that crowd, or the like.

Note that in one embodiment, the MAP application 32-1 may operate toroll-up the aggregate profiles for multiple crowds into a rolled-upaggregate profile for those crowds. The rolled-up aggregate profile maybe the average of the aggregate profiles of the crowds. For example, theMAP application 32-1 may roll-up the aggregate profiles for multiplecrowds at a POI and present the rolled-up aggregate profile for themultiple crowds at the POI to the user 20-1. In a similar manner, theMAP application 32-1 may provide a rolled-up aggregate profile for anAOI. In another embodiment, the MAP server 12 may roll-up crowds for aPOI or an AOI and provide the rolled-up aggregate profile in addition toor as an alternative to the aggregate profiles for the individualcrowds.

FIG. 29A is a flow chart illustrating step 2406 of FIG. 28 in moredetail according to one embodiment of the present disclosure. In thisembodiment, the crowd data returned by the MAP server 12 includesaggregate profiles for the crowds identified for the POI or the AOI. Inthis embodiment, upon receiving the crowd request, the MAP server 12triggers the crowd analyzer 58 to identify crowds relevant to thecurrent request, and then passes the identified crowds to theaggregation engine 60 in order to generate aggregate profiles for theidentified crowds.

More specifically, after the crowd analyzer 58 has identified the crowdsrelevant to the current request, the identified crowds are passed to theaggregation engine 60. The aggregation engine 60 selects a next crowd toprocess, which for the first iteration is the first crowd (step 2500-A).The aggregation engine 60 then selects the next user in the crowd (step2502-A). Next, the aggregation engine 60 compares the user profile ofthe user in the crowd to the user profile of the requesting user, whichfor this example is the user 20-1 of the mobile device 18-1, or a selectsubset of the user profile of the requesting user (step 2504-A). In someembodiments, the user 20-1 may be enabled to select a subset of his userprofile to be used for generation of the aggregate profile. For example,in the embodiment where user profiles are expressed as keywords in anumber of profile categories, the user 20-1 may select one or more ofthe profile categories to be used for aggregate profile generation. Whencomparing the user profile of the user in the crowd to the user profileof the user 20-1, the aggregation engine 60 identifies matches betweenthe user profile of the user in the crowd and the user profile of theuser 20-1 or the select subset of the user profile of the user 20-1. Inone embodiment, the user profiles are expressed as keywords in a numberof profile categories. The aggregation engine 60 may then make a list ofkeywords from the user profile of the user in the crowd that matchkeywords in user profile of the user 20-1 or the select subset of theuser profile of the user 20-1.

Next, the aggregation engine 60 determines whether there are more usersin the crowd (step 2506-A). If so, the process returns to step 2502-Aand is repeated for the next user in the crowd. Once all of the users inthe crowd have been processed, the aggregation engine 60 generates anaggregate profile for the crowd based on data resulting from thecomparisons of the user profiles of the users in the crowd to the userprofile of the user 20-1 or the select subset of the user profile of theuser 20-1 (step 2508-A). In an alternative embodiment, the aggregationengine 60 generates an aggregate profile for the crowd based on dataresulting from the comparisons of the user profiles of the users in thecrowd to a target user profile defined or otherwise specified by theuser 20-1. In one embodiment, the data resulting from the comparisons isa list of matching keywords for each of the users in the crowd. Theaggregate profile may then include a number of user matches over allkeywords and/or a ratio of the number of user matches over all keywordsto the number of users in the crowd. The number of user matches over allkeywords is a number of users in the crowd having at least one keywordin their user profile that matches a keyword in the user profile of theuser 20-1 or the select subset of the user profile of the user 20-1. Theaggregate profile may additionally or alternatively include, for eachkeyword in the user profile of the user 20-1 or the select subset of theuser profile of the user 20-1, a number of user matches for the keywordor a ratio of the number of user matches for the keyword to the numberof users in the crowd. Note that keywords in the user profile of theuser 20-1 or the select subset of the user profile of the user 20-1 thathave no user matches may be excluded from the aggregate profile. Inaddition, the aggregate profile for the crowd may include a total numberof users in the crowd.

The aggregate profile for the crowd may additionally or alternativelyinclude a match strength that is indicative of a degree of similaritybetween the user profiles of the users in the crowd and the user profileof the user 20-1. The match strength may be computed as a ratio of thenumber of user matches to the total number of users in the crowd.Alternatively, the match strength may be computed as a function of thenumber of user matches per keyword and keyword weights assigned to thekeywords. The keyword weights may be assigned by the user 20-1.

Once the aggregate profile of the crowd is generated, the aggregationengine 60 determines whether there are more crowds to process (step2510-A). If so, the process returns to step 2500-A and is repeated forthe next crowd. Once aggregate profiles have been generated for all ofthe crowds relevant to the current request, the aggregate profiles forthe crowds are returned (step 2512-A). More specifically, the aggregateprofiles are included in the crowd data returned to the MAP client 30-1in response to the current request.

Note that in some embodiments the user 20-1 is enabled to activate a“nearby POIs” feature. If this feature is enabled, the crowds identifiedby the crowd analyzer 58 and processed by the aggregation engine 60 toproduce corresponding aggregate profiles may also include crowds locatedat or near any nearby POIs. The nearby POIs may be POIs predefined bythe user 20-1, the MAP application 32-1, and/or the MAP server 12 thatare within a predefined distance from the POI or the AOI of the currentrequest.

FIG. 29B is a flow chart illustrating step 2406 of FIG. 28 in moredetail according to another embodiment of the present disclosure. Inthis embodiment, the crowd data returned by the MAP server 12 includesaggregate profiles for the crowds identified for the POI or the AOI. Inthis embodiment, upon receiving the crowd request, the MAP server 12triggers the crowd analyzer 58 to identify crowds relevant to thecurrent request, and then passes the identified crowds to theaggregation engine 60 in order to generate aggregate profiles for theidentified crowds.

More specifically, after the crowd analyzer 58 has identified the crowdsrelevant to the current request, the identified crowds are passed to theaggregation engine 60. The aggregation engine 60 selects a next crowd toprocess, which for the first iteration is the first crowd (step 2500-B).The aggregation engine 60 then selects the next user in the crowd (step2502-B). Next, the aggregation engine 60 compares the user profile ofthe user in the crowd to the user profile of the requesting user, whichfor this example is the user 20-1 of the mobile device 18-1, or a selectsubset of the user profile of the requesting user (step 2504-B). In someembodiments, the user 20-1 may be enabled to select a subset of his userprofile to be used for generation of the aggregate profile. For example,in the embodiment where user profiles are expressed as keywords in anumber of profile categories, the user 20-1 may select one or more ofthe profile categories to be used for aggregate profile generation. Whencomparing the user profile of the user in the crowd to the user profileof the user 20-1, the aggregation engine 60 identifies matches betweenthe user profile of the user in the crowd and the user profile of theuser 20-1 or the select subset of the user profile of the user 20-1. Inthis embodiment, the user profiles are expressed as keywords in a numberof profile categories. The aggregation engine 60 may then make a list ofkeywords from the user profile of the user in the crowd that matchkeywords in user profile of the user 20-1 or the select subset of theuser profile of the user 20-1.

Next, the aggregation engine 60 determines whether there are more usersin the crowd (step 2506-B). If so, the process returns to step 2502-Band is repeated for the next user in the crowd. Once all of the users inthe crowd have been processed, the aggregation engine 60 generates anaggregate profile for the crowd based on data resulting from thecomparisons of the user profiles of the users in the crowd to the userprofile of the user 20-1 or the select subset of the user profile of theuser 20-1 (step 2508-B). In an alternative embodiment, the aggregationengine 60 generates an aggregate profile for the crowd based on dataresulting from the comparisons of the user profiles of the users in thecrowd to a target user profile defined or otherwise specified by theuser 20-1. In this embodiment, the data resulting from the comparisonsis a list of matching keywords for each of the users in the crowd. Theaggregate profile may then include a number of user matches over allkeywords and/or a ratio of the number of user matches over all keywordsto the number of users in the crowd. The number of user matches over allkeywords is a number of users in the crowd having at least one keywordin their user profile that matches a keyword in the user profile of theuser 20-1 or the select subset of the user profile of the user 20-1. Theaggregate profile may additionally or alternatively include, for eachkeyword in the user profile of the user 20-1 or the select subset of theuser profile of the user 20-1, a number of user matches for the keywordor a ratio of the number of user matches for the keyword to the numberof users in the crowd. Note that keywords in the user profile of theuser 20-1 or the select subset of the user profile of the user 20-1 thathave no user matches may be excluded from the aggregate profile. Inaddition, the aggregate profile for the crowd may include a total numberof users in the crowd.

The aggregate profile for the crowd may additionally or alternativelyinclude a match strength that is indicative of a degree of similaritybetween the user profiles of the users in the crowd and the user profileof the user 20-1. The match strength may be computed as a ratio of thenumber of user matches to the total number of users in the crowd.Alternatively, the match strength may be computed as a function of thenumber of user matches per keyword and keyword weights assigned to thekeywords. The keyword weights may be assigned by the user 20-1.

Once the aggregate profile of the crowd is generated, in thisembodiment, the aggregation engine 60 compares the user profiles of theusers in the crowd to one another to determine N keywords having thehighest number of user matches among the users in the crowd (step2510-B). Here, N may be, for example, five. The aggregation engine 60then adds any of the N keywords that are not already in the aggregateprofile to the aggregate profile and flags those keywords asnon-matching keywords (step 2512-B). These keywords are flagged asnon-matching because they do not match any of the keywords in the userprofile, or select subset thereof, of the user 20-1. The non-matchingkeywords are preferably differentiated from the matching keywords in theaggregate profile when presented to the user 20-1. The non-matchingkeywords are particularly beneficial where there are few or no matchingkeywords between the user profile of the user 20-1 and the user profilesof the users in the crowd. In this situation, the non-matching keywordswould allow the user 20-1 to gain some understanding of the interests ofthe users in the crowd.

Next, the aggregation engine 60 determines whether there are more crowdsto process (step 2514-B). If so, the process returns to step 2500-B andis repeated for the next crowd. Once aggregate profiles have beengenerated for all of the crowds relevant to the current request, theaggregate profiles for the crowds are returned (step 2516-B). Morespecifically, the aggregate profiles are included in the crowd datareturned to the MAP client 30-1 in response to the current request.

Note that in some embodiments the user 20-1 is enabled to activate a“nearby POIs” feature. If this feature is enabled, the crowds identifiedby the crowd analyzer 58 and processed by the aggregation engine 60 toproduce corresponding aggregate profiles may also include crowds locatedat or near any nearby POIs. The nearby POIs may be POIs predefined bythe user 20-1, the MAP application 32-1, and/or the MAP server 12 thatare within a predefined distance from the POI or the AOI of the currentrequest.

FIG. 30 illustrates the operation of the system 10 of FIG. 1 to enablethe subscriber device 22 to request information regarding current crowdsaccording to one embodiment of the present disclosure. First, subscriberdevice 22 sends a crowd request to the MAP client 30-1 (step 2600). Thecrowd request is a request for current crowds at a specified POI or AOI.The crowd request may be initiated by the subscriber 24 at thesubscriber device 22 via the web browser 38 or a custom applicationenabled to access the MAP server 12. Preferably, the subscriber 24 isenabled to identify the POI or the AOI for the crowd request by, forexample, selecting the POI or the AOI on a map, selecting a crowd centerof an existing crowd as a POI, selecting a crowd location of an existingcrowd as a center of an AOI, selecting the POI or the AOI from apredefined list of POIs and/or AOIs, or the like. The predefined list ofPOIs and/or AOIs may be defined by, for example, the subscriber 24and/or the MAP server 12.

In response to receiving the crowd request from the subscriber device22, the MAP server 12 identifies one or more crowds relevant to thecrowd request (step 2602). More specifically, in one embodiment, thecrowd analyzer 58 performs a crowd formation process such as thatdescribed above in FIG. 22 to form one or more crowds relevant to thePOI or the AOI of the crowd request. In another embodiment, the crowdanalyzer 58 proactively forms crowds using a process such as thatdescribed above in FIGS. 24A through 24C and stores corresponding crowdrecords in the datastore 64 of the MAP server 12. Then, rather thanforming the relevant crowds in response to the crowd request, the crowdanalyzer 58 queries the datastore 64 to identify the crowds that arerelevant to the crowd request. The crowds relevant to the crowd requestmay be those crowds within or overlapping a bounding region, such as abounding box, for the crowd request. If the crowd request is for a POI,the bounding region is a geographic region of a predefined shape andsize centered at the POI. If the crowd request is for an AOI, thebounding region is the AOI.

Once the crowd analyzer 58 has identified the crowds relevant to thecrowd request, the MAP server 12 generates crowd data for the identifiedcrowds (step 2604). The crowd data for the identified crowds may includeaggregate profiles for the crowds, information characterizing thecrowds, or both. In addition, the crowd data may include the locationsof the crowds, the number of users in the crowds, the amount of time thecrowds have been located at or near the POI or within the AOI, or thelike. The MAP server 12 then returns the crowd data to the MAP client30-1 (step 2606). In the embodiment where the subscriber 24 accesses theMAP server 12 via the web browser 38 at the subscriber device 22, theMAP server 12 formats the crowd data into a suitable web format beforesending the crowd data to the subscriber device 22. The manner in whichthe crowd data is formatted depends on the particular implementation. Inone embodiment, the crowd data is overlaid upon a map. For example, inone embodiment, the MAP server 12 may provide the crowd data to thesubscriber device 22 via one or more web pages. Using the one or moreweb pages, crowd indicators representative of the locations of thecrowds may be overlaid on a map. The subscriber 24 may then select acrowd in order to view additional crowd data regarding that crowd suchas, for example, the aggregate profile of that crowd, characteristics ofthat crowd, or the like. Upon receiving the crowd data, the subscriberdevice 22 presents the crowd data to the subscriber 24 (step 2608). Notethat in one embodiment, the MAP server 12 may roll-up the aggregateprofiles for multiple crowds at a POI or in an AOI to provide arolled-up aggregate profile that may be returned in addition to or as analternative to the aggregate profiles of the individual crowds.

It should be noted that in some embodiments, the subscriber 24 may beenabled to specify filtering criteria via the web browser 38 or a customapplication for interacting with the MAP server 12. For example, thesubscriber 24 may specify filtering criteria regarding types of crowdsin which the subscriber 24 is or is not interested. For instance, thecrowd data may be presented to the subscriber 24 via one or more webpages that enable the subscriber 24 to select a filtering feature. Inresponse, a list of keywords appearing in the user profiles of thecrowds identified as being relevant to the current request may bepresented to the subscriber 24. The subscriber 24 may then specify oneor more keywords from the list such that crowds having users with userprofiles that do not include any of the specified keywords are filtered,or removed, and are therefore not considered when generating the crowddata in response to a crowd request.

FIG. 31 is a flow chart illustrating step 2604 of FIG. 30 in more detailaccording to one embodiment of the present disclosure. In thisembodiment, the crowd data returned by the MAP server 12 includesaggregate profiles for the crowds identified for the POI or the AOI. Inthis embodiment, upon receiving the crowd request, the MAP server 12triggers the crowd analyzer 58 to identify crowds relevant to the crowdrequest, and then passes the identified crowds to the aggregation engine60 in order to generate aggregate profiles for the identified crowds.

More specifically, after the crowd analyzer 58 has identified the crowdsrelevant to the crowd request, the identified crowds are passed to theaggregation engine 60. The aggregation engine 60 selects a next crowd toprocess, which for the first iteration is the first crowd (step 2700).The aggregation engine 60 then generates an aggregate profile for thecrowd based on a comparison of the user profiles of the users in thecrowd to one another (step 2702). Note that in an alternativeembodiment, the aggregation engine 60 then generates an aggregateprofile for the crowd based on a comparison of the user profiles of theusers in the crowd to a target user profile defined by the subscriber24.

In one embodiment, in order to generate the aggregate profile for thecrowd, the user profiles are expressed as keywords for each of a numberof profile categories. Then, the aggregation engine 60 may determine anaggregate list of keywords for the crowd. The aggregate list of keywordsis a list of all keywords appearing in the user profiles of the users inthe crowd. The aggregate profile for the crowd may then include a numberof user matches for each keyword in the aggregate list of keywords forthe crowd. The number of user matches for a keyword is the number ofusers in the crowd having a user profile that includes that keyword. Theaggregate profile may include the number of user matches for allkeywords in the aggregate list of keywords for the crowd or the numberof user matches for keywords in the aggregate list of keywords for thecrowd having more than a predefined number of user matches (e.g., morethan 1 user match). The aggregate profile may also include the number ofusers in the crowd. In addition or alternatively, the aggregate profilemay include, for each keyword in the aggregate list or each keyword inthe aggregate list having more than a predefined number of user matches,a ratio of the number of user matches for the keyword to the number ofusers in the crowd.

Once the aggregate profile of the crowd is generated, the aggregationengine 60 determines whether there are more crowds to process (step2704). If so, the process returns to step 2700 and is repeated for thenext crowd. Once aggregate profiles have been generated for all of thecrowds relevant to the crowd request, the aggregate profiles for thecrowds are returned (step 2706). Note that in some embodiments thesubscriber 24 is enabled to activate a “nearby POIs” feature. If thisfeature is enabled, the crowds identified by the crowd analyzer 58 andprocessed by the aggregation engine 60 to produce correspondingaggregate profiles may also include crowds located at or near any nearbyPOIs. The nearby POIs may be POIs predefined by the subscriber 24 and/orthe MAP server 12 that are within a predefined distance from the POI orthe AOI of the crowd request.

FIGS. 32A through 32E illustrate a GUI 198 for an exemplary embodimentof the MAP application 32-1 of the mobile device 18-1 (FIG. 1). Asillustrated in FIG. 32A, the GUI 198 includes a settings screen 198-1that is presented in response to selection of a corresponding settingsbutton 200 by the user 20-1. A navigation button 202 may be selected toview a map and perform navigation functions such as obtaining directionsto a desired location. A list button 204 enables the user 20-1 to view alist of friends, crowds, POIs, and AOIs, as discussed below. Regardingthe settings displayed in the settings screen 198-1 of the GUI 198, theuser 20-1 is enabled to provide his Facebook® login information which,as described above, enables the user profile of the user 20-1 to beobtained from the Facebook® social networking service. In this example,the user 20-1 has already been logged in to Facebook. As such, the user20-1 may logout of Facebook by selecting a logout button 206. Inaddition, by selecting a profile setting 208, the user 20-1 is enabledto view his profile and select one or more profile categories to be usedfor aggregate profile generation.

The settings screen 198-1 also enables the user 20-1 to configure anumber of privacy settings. Namely, the settings screen 198-1 enablesthe user 20-1 to set a stealth mode switch 210 to either an on positionor an off position. When the stealth mode switch 210 is in the onposition, the location of the user 20-1 is not reported to the friendsof the user 20-1. However, the location of the user 20-1 is stillreported for use by the MAP server 12. The privacy settings also includea location refresh setting 212 that enables the user 20-1 to configurehow often location updates are to be sent by the MAP application 32-1.Lastly, the settings screen 198-1 includes an alerts setting 214 thatenables the user 20-1 to configure one or more alerts. As discussedbelow, an alert can be tied to a particular POI or AOI such that theuser 20-1 is alerted, or notified, when a crowd at the particular POI orAOI satisfies one or more specified criteria. Alternatively, an alertcan be tied to a particular crowd such that the user 20-1 is alerted, ornotified, when the crowd satisfies one or more specified criteria.

Returning to the profile setting 208, if the user 20-1 selects theprofile setting 208, a user profile screen 198-2 is presented to theuser 20-1 via the GUI 198, as illustrated in FIG. 32B. The user profilescreen 198-2 shows a number of profile categories 216A through 216E andcorresponding lists of keywords 218A through 218E, which form the userprofile of the user 20-1. The user 20-1 is enabled to select one or moreof the profile categories 216A through 216E to be used for aggregateprofile generation (i.e., comparison to user profiles for historyobjects and crowds to create corresponding aggregate profiles for theuser 20-1). In this example, the user 20-1 has selected his “MyInterests” profile category 216C, where the corresponding list ofkeywords 218C define general interests of the user 20-1. In the userprofile screen 198-2, the user 20-1 can return to the settings screen198-1 by selecting a settings button 220.

FIGS. 32C and 32D illustrate a list screen 198-3 that is presented tothe user 20-1 via the GUI 198 in response to selecting the list button204. The list screen 198-3 includes a friends button 222, a crowdsbutton 224, a POI button 226, an areas button 228, and an all button230. The list screen 198-3 enables the user 20-1 to view a list of hisfriends by selecting the friends button 222, a list of crowds at POIs orwithin AOIs of the user 20-1 by selecting the crowds button 224, a listof POIs of the user 20-1 by selecting the POI button 226, or a list ofAOIs of the user 20-1 by selecting the areas button 228. In addition,the list screen 198-3 enables the user 20-1 to view a list that includesthe friends of the user, the crowds at POIs or within AOIs of the user20-1, the POIs of the user 20-1, and the AOIs of the user 20-1 byselecting the all button 230.

In this example, the user 20-1 has selected the all button 230. As such,the list screen 198-3 presents an AOI list 232 that includes a number ofAOIs previously defined by the user 20-1. Note that each of the AOIs maybe a static AOI defining a static geographic area or a dynamic AOI thatis defined relative to a dynamic location such as a location of a friendof the user 20-1. For instance, in this example, the “Near JackShephard” AOI is a geographic area of a defined shape and size that iscentered at the current location of the user's friend Jack Shephard.Note that in one embodiment, persons whose current locations may be usedfor dynamic AOIs are limited to the friends of the user 20-1. The user20-1 may select an AOI from the AOI list 232 in order to view crowd datafor the AOI. For example, by selecting the My Neighborhood AOI, the GUI198 may present a map including the My Neighborhood AOI. Crowds relevantto the My Neighborhood AOI are presented on the map. The user 20-1 maythen select a desired crowd in order to view detailed informationregarding that crowd such as, for example, the aggregate profile of thecrowd, characteristics of the crowd, or both.

The list screen 198-3 also presents a crowds list 234 that includes anumber of crowds that are at the POIs or within the AOIs of the user20-1. In this example, there are twelve crowds. The GUI 198 enables theuser 20-1 to select a crowd from the crowds list 234 in order to viewadditional information regarding the crowd. For example, by selectingthe Crowd of 6, the user 20-1 may be presented with a map showing thecurrent location of the Crowd of 6 and detailed information regardingthe Crowd of 6 such as, for example, the aggregate profile of the Crowdof 6, characteristics of the Crowd of 6, or both.

The list screen 198-3 also includes a friends list 236, as illustratedin FIG. 32D. The user 20-1 may select a friend from the friends list 236in order to view crowds nearby that friend. In other words, the currentlocations of the friends of the user 20-1 are treated as temporary ordynamic POIs such that crowd data for current locations of the friendsof the user 20-1 is obtained from the MAP server 12. In addition, theuser 20-1 may choose to define an AOI centered at the current locationof a friend to create a dynamic AOI, as discussed above. The friendslist 236 also presents the current location of the friends of the user20-1 relative to the current location of the user 20-1.

The list screen 198-3 also includes a POI list 238 that includes anumber of POIs of the user 20-1. The user 20-1 may select a POI from thePOI list 238 in order to view crowd data for the POI. For example, byselecting the Steve's house POI, the GUI 198 may present a map includingthe Steve's house POI. Crowds at or near the Steve's house POI arepresented on the map. The user 20-1 may then select a desired crowd inorder to view detailed information regarding that crowd such as, forexample, the aggregate profile of the crowd, characteristics of thecrowd, or both. Lastly, returning to FIG. 32C, the list screen 198-3includes a You item 240 that may be selected by the user 20-1 to accessthe user profile screen 198-2 (FIG. 32B).

FIG. 32E is a crowd data display screen 198-4 presented by the GUI 198.In this example, the user 20-1 has selected the Around You AOI from theAOI list 232 (FIG. 32C). As a result, the GUI 198 presents the crowddata display screen 198-4 for the Around You AOI. The crowd data displayscreen 198-4 includes a map area 242. In this example, the currentlocation of the user 20-1 is used as the center of the Around You AOI.The current location of the user 20-1 is represented in the map area 242by a corresponding indicator 244. Crowds in the Around You AOI arerepresented in the map area by crowd indicators 246 through 250. In thisembodiment, the crowd indictors 246 through 250 show the locations ofthe crowds as well as match strengths for the crowds. The locations ofthe crowds are included in the crowd data. The match strengths for thecrowds may be included in the aggregate profiles for the crowds or maybe determined based on the aggregate profiles for the crowds. In thisembodiment, the match strength of a crowd is computed as a ratio of thenumber of user matches over all keywords to the number of users in thecrowd. A ratio of one results in a highest match strength, and a ratioof zero results in a lowest match strength.

Using the GUI 198, the user 20-1 is enabled to select a particular crowdin the map area 242 to view more detailed information for that crowd ina crowd detail area 252 of the crowd data display screen 198-4. In thisexample, the user 20-1 has selected the crowd indicator 246. As aresult, more detailed information for the crowd represented by the crowdindicator 246 is presented in the crowd detail area 252. The moredetailed information for the crowd is from the crowd data for the crowdor derived from the crowd data for the crowd. In this example, theaggregate profile of the crowd is used to derive the match strength forthe crowd, and the match strength is presented in the crowd detail area252. In addition, the crowd size and number of user matches over allkeywords are obtained from the aggregate profile for the crowd andpresented in the crowd detail area 252. In this example, a qualityfactor for the crowd is also presented. As discussed below in detail,the quality factor of the crowd may be an average of a quality orconfidence of the current locations of the users in the crowd. Stillfurther, the crowd data display screen 198-4 includes a keyword matchesarea 254 for presenting keyword matches for the selected crowd. In thisexample, a font size of the keywords in the keyword matches area 254reflects the number of user matches for that keyword. Therefore, in thisexample, the number of user matches for the keyword “technology” isgreater than the number of user matches for the keyword “books.”

FIGS. 33A through 33C illustrate an exemplary web interface 256 providedby the MAP server 12 and presented to the subscriber 24 at thesubscriber device 22. The web interface 256 includes a number of tabs258 through 272, namely, a home tab 258, a realtime tab 260, ahistorical tab 262, a watch zones tab 264, an alerts tab 266, a filterstab 268, a reports tab 270, and an account tab 272. The home tab 258enables the subscriber 24 to view a home screen. The home screen mayinclude any desired information such as, for example, a link to aFrequently Asked Question (FAQ) page, instructions on how to use the webinterface 256, or the like. The realtime tab 260 enables the subscriberto view realtime crowd data for POIs and/or AOIs of the subscriber 24.The historical tab 262 enables the subscriber 24 to view historical datafor a POI or an AOI in a time context and/or a geographic context in themanner described above. The watch zones tab 264 enables the subscriber24 to select POIs and/or AOIs of interest to the subscriber 24. Thealerts tab 266 enables the subscriber 24 to configure one or morealerts. The filters tab 268 enables the subscriber 24 to configurefilters and/or select filters to be applied to the crowd data in therealtime or historical view. The reports tab 270 enables the subscriber24 to access reports previously generated for crowds of interest, POIs,and/or AOIs. Lastly, the account tab 272 enables the subscriber 24 tomanage the subscriber's account.

More specifically, FIG. 33A illustrates the web interface 256 when therealtime tab 260 has been selected by the subscriber 24. When therealtime tab 260 is selected, the web interface 256 presents a map area274 that shows an AOI 276 and a number of crowds 278 through 282currently located within the AOI 276. In addition, in this exemplaryembodiment, crowds 284 and 286 that are outside the AOI 276 are alsoillustrated. The crowds 284 and 286 are crowds located at other POIs orwithin other AOIs of the subscriber 24 that are not currently beingviewed by the subscriber 24. The subscriber 24 may view another POI orAOI by selecting the desired POI or AOI from a list presented inresponse to selection of a button 288. In this example, POIs and AOIsare generically referred to as watch zones.

In this example, the subscriber 24 selects the crowd 278. In response,the web interface 256 presents an aggregate profile window 290 to thesubscriber 24, as illustrated in FIG. 33B. The aggregate profile window290 presents an aggregate profile of the crowd 278, where in thisembodiment the aggregate profile is in the form of an interest histogramshowing the number of user matches in the crowd 278 for each of a numberof keywords. The subscriber 24 may be enabled to create an alert for thecrowd 278 by selecting a create an alert button 292. In response, thesubscriber 24 may be enabled to utilize the keywords in the aggregateprofile window 290 to create an alert. For example, the subscriber 24may create an alert such that the subscriber 24 is notified when thenumber of user matches for the keyword “Sushi” in the crowd 278 reachesone hundred. The subscriber 24 may also be enabled to create a reportfor the crowd 278 by selecting a create a report button 294. The reportmay, for example, include details about the crowd 278 such as, forexample, the location of the crowd 278, the size of the crowd 278, theaggregate profile of the crowd 278, the current time and date, or thelike, where the report may be saved or printed by the subscriber 24.

In addition, the subscriber 24 may be enabled to create a filter byselecting a create a filter button 296. In response to selecting thecreate a filter button 296, a new filter screen 298 is presented to thesubscriber 24, as illustrated in FIG. 33C. The subscriber 24 may thenselect keywords from the interest histogram for the crowd 278 to be usedfor the filter. In addition, the subscriber 24 may be enabled to add newkeywords to the filter by selecting an add keywords button 300. Once thesubscriber 24 has configured the filter, the subscriber 24 is enabled tocreate the filter by selecting a create button 302. Once the filter iscreated, the filter may be used to filter crowds for any AOI or POI ofthe subscriber 24.

FIGS. 34 through 45 describe the operation of the crowd analyzer 58 ofthe MAP server 12 to characterize crowds according to another embodimentof the present disclosure. More specifically, the crowd analyzer 58 maydetermine a degree-of-fragmentation, best and worst case average DOS,and/or a degree of bidirectionality for crowds. This information maythen be included in crowd data for those crowds returned to the mobiledevices 18-1 through 18-N and/or the subscriber device 22. In additionor alternatively, the data characterizing crowds may be used to filtercrowds. For example, a filter may be applied such that crowds having aworst-case average DOS greater than a defined threshold are notpresented to a user/subscriber. The filtering may be performed by theMAP server 12 before returning crowd data to the requesting device(i.e., one of the mobile devices 18-1 through 18-N, the subscriberdevice 22, or a device hosting the third-party service 26).Alternatively, the filtering may be performed by the mobile devices 18-1through 18-N, the subscriber device 22, or a device hosting thethird-party service 26.

FIG. 34 is a flow chart illustrating a spatial crowd fragmentationprocess according to one embodiment of the present disclosure. Thisprocess is similar to the spatial crowd formation process discussedabove with respect to FIG. 22. First, the crowd analyzer 58 creates acrowd fragment of one user for each user in a crowd (step 2800). Notethat this spatial crowd fragmentation process may be performedreactively in response to a current request for crowd data for a POI oran AOI or performed proactively. Next, the crowd analyzer 58 determinesthe two closest crowd fragments in the crowd (step 2802) and a distancebetween the two closest crowd fragments (step 2804). The distancebetween the two closest crowd fragments is the distance between thecrowd fragment centers of the two closest crowd fragments. The crowdfragment center for a crowd fragment having only one user is the currentlocation of that one user.

The crowd analyzer 58 then determines whether the distance between thetwo closest crowd fragments is less than an optimal inclusion distancefor a crowd fragment (step 2806). In one embodiment, the optimalinclusion distance for a crowd fragment is a predefined static value. Inanother embodiment, the optimal inclusion distance of the crowd mayvary. For example, if the spatial crowd formation process of FIGS. 24Athrough 24D is used for proactive crowd formation, then the optimalinclusion distance for the crowd may vary. As such, the optimalinclusion distance for a crowd fragment within the crowd may be definedas a fraction of the optimal inclusion distance of the crowd such thatthe optimal inclusion distance for a crowd fragment within the crowdvaries along with the optimal inclusion distance for the crowd itself.

If the distance between the two closest crowd fragments is less than theoptimal inclusion distance for a crowd fragment, then the two closestcrowd fragments are combined (step 2808) and a new crowd fragment centeris computed for the resulting crowd fragment (step 2810). The crowdfragment center may be computed using, for example, a center of massalgorithm. At this point the process returns to step 2802 and isrepeated. Once the two closest crowd fragments in the crowd areseparated by more than the optimal inclusion distance for a crowdfragment, the process ends. At this point, the crowd analyzer 58 hascreated the crowd fragments or defined the crowd fragments for thecrowd. The crowd analyzer 58 may then represent the degree offragmentation of the crowd based on the number of crowd fragments in thecrowd and, optionally, an average number of users per crowd fragment.The degree of fragmentation of the crowd may be included in the crowddata returned to the requesting device in response to a crowd requestfor a POI or an AOI to which the crowd is relevant.

FIGS. 35A and 35B graphically illustrate the spatial crowd fragmentationprocess of FIG. 34 for an exemplary crowd 304 having bounding box 305.FIG. 35A illustrates the crowd 304 before spatial crowd fragmentation.FIG. 35B illustrates the crowd 304 after spatial crowd fragmentation. Asillustrated, after spatial crowd fragmentation, the crowd 304 includes anumber of crowd fragments 306 through 314. As such, the crowd 304 has adegree of fragmentation of five crowd fragments with an average ofapproximately 2 users per crowd fragment. Thus, the crowd 304 has amoderately high degree of fragmentation. The highest degree offragmentation for the crowd 304 would be to have eleven crowd fragmentswith an average of one user per crowd fragment. The lowest degree offragmentation for the crowd 304 would be to have one crowd fragment withan average of eleven users per crowd fragment.

FIG. 36 illustrates a connectivity-based crowd fragmentation processaccording to one embodiment of the present disclosure. First, the crowdanalyzer 58 creates a crowd fragment for each user in the crowd (step2900). Note that this connectivity-based crowd fragmentation process maybe performed reactively in response to a current request for crowd datafor a POI or an AOI or performed proactively. Next, the crowd analyzer58 selects a next pair of crowd fragments in the crowd (step 2902) andthen selects one user from each of those crowd fragments (step 2904).The crowd analyzer 58 then determines a DOS between the users from thepair of crowd fragments (step 2906). More specifically, as will beappreciated by one of ordinary skill in the art, DOS is a measure of thedegree to which the two users are related in a social network (e.g., theFacebook® social network, the MySpace® social network, or the LinkedIN®social network). The two users have a DOS of one if one of the users isa friend of the other user, a DOS of two if one of the users is a friendof a friend of the other user, a DOS of three if one of the users is afriend of a friend of a friend of the other user, etc. If the two usersare not related in a social network or have an unknown DOS, the DOS forthe two users is set to a value equal to or greater than the maximum DOSfor a crowd fragment.

The crowd analyzer 58 then determines whether the DOS between the twousers is less than a predefined maximum DOS for a crowd fragment (step2908). For example, the predefined maximum DOS may be three. However,other maximum DOS values may be used to achieve the desired crowdfragmentation. If the DOS between the two users is not less than thepredefined maximum DOS, the process proceeds to step 2916. If the DOSbetween the two users is less than the predefined maximum DOS, the crowdanalyzer 58 determines whether a bidirectionality requirement issatisfied (step 2910). The bidirectionality requirement specifieswhether the relationship between the two users must be bidirectional(i.e., the first user must directly or indirectly know the second userand the second user must directly or indirectly know the first user).Bidirectionality may or may not be required depending on the particularembodiment. If the two users satisfy the bidirectionality requirement,the crowd analyzer 58 combines the pair of crowd fragments (step 2912)and computes a new crowd fragment center for the resulting crowdfragment (step 2914). The process then returns to step 2902 and isrepeated for a next pair of crowd fragments. If the two users do notsatisfy the bidirectionality requirement, the process proceeds to step2916.

At this point, whether proceeding from step 2908 or step 2910, the crowdanalyzer 58 determines whether all user pairs from the two crowdfragments have been processed (step 2916). If not, the process returnsto step 2904 and is repeated for a new pair of users from the two crowdfragments. If all user pairs from the two crowd fragments have beenprocessed, the crowd analyzer 58 then determines whether all crowdfragments have been processed (step 2918). If not, the process returnsto step 2902 and is repeated until all crowd fragments have beenprocessed. Once this process is complete, the crowd analyzer 58 hasdetermined the number of crowd fragments in the crowd. The degree offragmentation of the crowd may then be provided as the number of crowdfragments and the average number of users per crowd fragment.

FIGS. 37A and 37B graphically illustrate the connectivity-based crowdfragmentation process of FIG. 36. FIG. 37A illustrates a crowd 316having a number of users and a bounding box 317. FIG. 37B illustratesthe crowd 316 after the connectivity-based crowd fragmentation processhas been performed. As illustrated, there are three crowd fragmentsresulting from the connectivity-based crowd fragmentation process.Namely, crowd fragment A has four users marked as “A,” crowd fragment Bhas five users marked as “B,” and crowd fragment C has three usersmarked as “C.” As illustrated, the users in a particular crowd fragmentmay not be close to one another spatially since, in this embodiment,there is no spatial requirement for users of the crowd fragment otherthan that the users of the crowd fragment are in the same crowd.

FIG. 38 is a flow chart illustrating a recursive crowd fragmentationprocess that uses both spatial crowd fragmentation andconnectivity-based crowd fragmentation according to one embodiment ofthe present disclosure. First, the crowd analyzer 58 performs a spatialcrowd fragmentation process to create a number of crowd fragments for acrowd (step 3000). The spatial crowd fragmentation process may be thespatial crowd fragmentation process of FIG. 34. The crowd analyzer 58then selects a next crowd fragment of the crowd fragments created forthe crowd (step 3002). Next, the crowd analyzer 58 performs aconnectivity-based crowd fragmentation process to create a number ofsub-fragments for the crowd fragment of the crowd (step 3004). Theconnectivity-based crowd fragmentation process may be theconnectivity-based crowd fragmentation process of FIG. 36. The crowdanalyzer 58 then determines whether the last crowd fragment of the crowdhas been processed (step 3006). If not, the process returns to step 3002and is repeated until the last crowd fragment of the crowd has beenprocessed. At that point, the process is complete. The degree offragmentation for the crowd may then include the number of sub-fragmentsand average number of users per sub-fragment for each crowd fragment.

FIG. 39 is a flow chart illustrating a recursive crowd fragmentationprocess that uses both spatial crowd fragmentation andconnectivity-based crowd fragmentation according to another embodimentof the present disclosure. First, the crowd analyzer 58 performs aconnectivity-based crowd fragmentation process to create a number ofcrowd fragments for a crowd (step 3100). The connectivity-based crowdfragmentation process may be the connectivity-based crowd fragmentationprocess of FIG. 36. The crowd analyzer 58 then selects a next crowdfragment of the crowd fragments created for the crowd (step 3102). Next,the crowd analyzer 58 performs a spatial crowd fragmentation process tocreate a number of sub-fragments for the crowd fragment of the crowd(step 3104). The spatial crowd fragmentation process may be the spatialcrowd fragmentation process of FIG. 34. The crowd analyzer 58 thendetermines whether the last crowd fragment of the crowd has beenprocessed (step 3106). If not, the process returns to step 3102 and isrepeated until the last crowd fragment of the crowd has been processed.At that point, the process is complete. The degree of fragmentation forthe crowd may then include the number of sub-fragments and averagenumber of users per sub-fragment for each crowd fragment.

FIGS. 40A and 40B illustrate an exemplary graphical representation ofthe degree of fragmentation for a crowd. This exemplary graphicalrepresentation may be presented by the MAP application 32-1 based oncorresponding crowd data provided by the MAP server 12 in response to acrowd request or presented by the MAP server 12 to the subscriber 24 viathe web browser 38 of the subscriber device 22. FIG. 40A illustrates agraphical representation of the degree of fragmentation for a crowdhaving two crowd fragments with an average of twenty-five users percrowd fragment. FIG. 40B illustrates a graphical representation of thedegree of fragmentation for a crowd having twenty-five crowd fragmentswith an average of two users per crowd fragment.

FIG. 41 is a flow chart for a process for determining a best-case andworst-case average DOS for a crowd fragment of a crowd according to oneembodiment of the present disclosure. The crowd analyzer 58 counts thenumber of 1 DOS, 2 DOS, . . . , M DOS relationships in a crowd fragment(step 3200) and the number of user pairs in the crowd fragment for whichexplicit relationships are not defined or known (step 3202). Morespecifically, for each pair of users in the crowd fragment, the crowdanalyzer 58 determines the DOS between the pair of users if the DOSbetween the pair of user is known or determines that the DOS between thepair of users is not defined or known if the DOS between the pair ofusers is in fact not defined or known. Based on these determinations,the crowd analyzer 58 counts the number of user pairs having a DOS of 1,the number of user pairs having a DOS of 2, etc. In addition, the crowdanalyzer 58 counts the number of user pairs for which no relationship isdefined or known.

The crowd analyzer 58 then computes a best-case average DOS for thecrowd fragment using a best-case DOS for the user pairs in the crowdfragment for which explicit relationships are not defined (step 3204).In this embodiment, the best-case average DOS is 1. The best-caseaverage DOS may computed as:

${{AverageDOS}_{BestCase} = \frac{{\sum\limits_{i = 1}^{M}\; \left( {i \cdot {DOS\_ count}_{i}} \right)} + {{DOS}_{BestCase} \cdot {Num\_ Unknown}}}{{\sum\limits_{i = 1}^{M}\; \left( {DOS\_ count}_{i} \right)} + {Num\_ Unknown}}},$

where AverageDOS_(Bestcase) is the best-case average DOS for the crowdfragment, DOS_count_(i) is the number of user pairs for the ith DOS,DOS_(Bestcase) is the best-case DOS, and Num_Unknown is the number ofuser pairs for which a relationship is not defined or is unknown.

The crowd analyzer 58 also computes the worst-case average DOS for thecrowd fragment using a worst-case DOS for the user pairs in the crowdfragment for which explicit relationships are not defined (step 3206).In this embodiment, the worst-case DOS is a greatest possible DOS thatthe crowd analyzer 58 considers, which may be, for example, a DOS ofgreater than or equal to 7. For instance, the worst-case DOS may be 10.However, other values for the worst-case DOS may be used. The worst-caseaverage DOS may computed as:

${{AverageDOS}_{WorstCase} = \frac{{\sum\limits_{i = 1}^{M}\; \left( {i \cdot {DOS\_ count}_{i}} \right)} + {{DOS}_{WorstCase} \cdot {Num\_ Unknown}}}{{\sum\limits_{i = 1}^{M}\; \left( {DOS\_ count}_{i} \right)} + {Num\_ Unknown}}},$

where AverageDOS_(WorstCase) is the worst-case average DOS for the crowdfragment, DOS_count_(i) is the number of user pairs for the ith DOS,DOS_(WorstCase) is the worst-case DOS, and Num_Unknown is the number ofuser pairs for which a relationship is not defined or is unknown.

FIG. 42 is a more detailed flow chart illustrating the process fordetermining a best-case and worst-case average DOS for a crowd fragmentaccording to one embodiment of the present disclosure. First, the crowdanalyzer 58 selects the next user in the crowd fragment, which for thefirst iteration is the first user in the crowd fragment (step 3300), andclears a found member list (step 3302). The crowd analyzer 58 then setsa current DOS to one (step 3304). Next, the crowd analyzer 58 selects anext friend of the user (step 3306). Note that, in one embodiment,information identifying the friends of the user are obtained from theone or more profile servers 14 along with the user profile of the user.The crowd analyzer 58 then determines whether the friend of the user isalso a member of the crowd fragment (step 3308). If not, the processproceeds to step 3314. If the friend is also a member of the crowdfragment, the crowd analyzer 58 determines whether the friend is alreadyin the found member list (step 3310). If so, the process proceeds tostep 3314. If the friend is also a member of the crowd fragment and isnot already in the found member list, the crowd analyzer 58 increments afound count for the current DOS and adds the friend to the found memberlist (step 3312). At this point, whether proceeding from step 3308 orstep 3310, the crowd analyzer 58 then determines whether the user hasmore friends to process (step 3314). If so, the process returns to step3306 and is repeated for the next friend of the user.

Once all of the friends of the user have been processed, the crowdanalyzer 58 performs steps 3306 through 3314 recursively for each newlyfound friend, incrementing the current DOS for each recursion, up to amaximum number of recursions (step 3316). Newly found friends arefriends added to the found member list in the iteration or recursion ofsteps 3306 through 3314 just completed. In more general terms, steps3306 through 3316 operate to find friends of the user selected in step3300 that are also members of the crowd fragment and increment the foundcount for a DOS of 1 for each of the found friends of the user. Then,for each friend of the user that was found to also be a member of thecrowd fragment, the crowd analyzer 58 finds friends of that friend ofthe user that are also members of the crowd fragment and increments thefound count for a DOS of 2 for each of the found friends of the friendof the user. The process continues in this manner to count the number ofuser relationships between the user selected in step 3300 and othermembers in the crowd fragment up to the Mth DOS.

Next, the crowd analyzer 58 determines a count of users in the crowdfragment that were not found as being directly or indirectly related tothe user selected in step 3300 (step 3318). More specifically, bylooking at the found member list and the total number of users in thecrowd fragment, the crowd analyzer 58 is enabled to determine the countof users in the crowd fragment that were not found as being directly orindirectly related to the user.

At this point, the crowd analyzer 58 determines whether there are moreusers in the crowd fragment to process (step 3320). If so, the processreturns to step 3300 and is repeated for the next user in the crowdfragment. Once all of the users in the crowd fragment have beenprocessed, the crowd analyzer 58 computes a best-case average DOS forthe crowd fragment (step 3322). Again, in one embodiment, the best-caseaverage DOS for the crowd fragment is computed as:

${{AverageDOS}_{BestCase} = \frac{{\sum\limits_{i = 1}^{M}\; \left( {i \cdot {found\_ count}_{DOSi}} \right)} + {{DOS}_{BestCase} \cdot {Num\_ Unknown}}}{{\sum\limits_{i = 1}^{M}\; \left( {found\_ count}_{DOSi} \right)} + {Num\_ Unknown}}},$

where AverageDOS_(Bestcase) is the best-case average DOS for the crowdfragment, found_count_(Dosi) is the found count for the ith DOS,DOS_(Bestcase) is the best-case DOS which may be set to, for example, 1,and Num_Unknown is the total count of user pairs in the crowd fragmentthat were not found as being directly or indirectly related.

In addition, the crowd analyzer 58 computes a worst-case average DOS forthe crowd fragment (step 3324). Again, in one embodiment, the worst-caseaverage DOS for the crowd fragment is computed as:

${{AverageDOS}_{WorstCase} = \frac{{\sum\limits_{i = 1}^{M}\; \left( {i \cdot {found\_ count}_{DOSi}} \right)} + {{DOS}_{WorstCase} \cdot {Num\_ Unknown}}}{{\sum\limits_{i = 1}^{M}\; \left( {found\_ count}_{DOSi} \right)} + {Num\_ Unknown}}},$

where AverageDOS_(WorstCase) is the worst-case average DOS for the crowdfragment, found_count_(Dosi) is the found count for the ith DOS,DOS_(WorstCase) is the worst-case DOS which may be set to, for example,10, and Num_Unknown is the total count of user pairs in the crowdfragment that were not found as being directly or indirectly related. Atthis point the process is complete and the best-case and worst-caseaverage DOS for the crowd fragment may be returned as part of the crowddata for the corresponding crowd. It should be noted that while theprocesses of FIGS. 41 and 42 were described above as being performed ona crowd fragment, the same processes may be performed on a crowd inorder to determine a best-case and worst-case average DOS for the crowd.

FIGS. 43A through 43D illustrate an exemplary graphical representationof the best-case and worst-case average DOS for a crowd fragmentaccording to one embodiment of the present disclosure. Such graphicalrepresentations may be presented to the mobile users 20-1 through 20-Nby the MAP applications 32-1 through 32-N or presented to the subscriber24 by the MAP server 12 via the web browser 38 at the subscriber device22 based on data included in the crowd data for corresponding crowds.FIG. 43A illustrates the graphical representation for a crowd fragmentwherein all users in the crowd fragment are friends with one another. Assuch, both the best-case and worst-case average DOS for the crowdfragment are 1. FIG. 43B illustrates the graphical representation for acrowd fragment wherein the best-case average DOS is 2 and the worst-caseaverage DOS is 3. FIG. 43C illustrates the graphical representation fora crowd fragment wherein the best-case average DOS is 4 and theworst-case average DOS is greater than 7. Lastly, FIG. 43D illustratesthe graphical representation for a crowd fragment wherein the best-caseaverage DOS is 6 and the worst-case average DOS is 7. Again, while inthese examples the graphical representations are for the best-case andworst-case average DOS for a crowd fragment, best-case and worst-caseaverage DOS for a crowd may additionally or alternatively be computed bythe MAP server 12 and presented to the users 20-1 through 20-N or thesubscriber 24.

FIG. 44 is a flow chart for a process of determining a degree ofbidirectionality of relationships between users in a crowd fragmentaccording to one embodiment of the present disclosure. Note, however,that this same process may be used to determine a degree ofbidirectionality of relationships between users in a crowd. First, thecrowd analyzer 58 selects the next user in a crowd fragment, which forthe first iteration is the first user in the crowd fragment (step 3400).The crowd analyzer 58 then selects the next friend of the user (step3402). Again, note that friends of the users 20-1 through 20-N may havebeen previously been obtained from the one or more profile servers 14along with the user profiles of the users 20-1 through 20-N and providedto the MAP server 12. The crowd analyzer 58 then determines whether thefriend of the user is a member of the crowd fragment (step 3404). Ifnot, the process proceeds to step 3412. If the friend of the user is amember of the crowd fragment, the crowd analyzer 58 increments aconnection count (step 3406). In addition, the crowd analyzer 58determines whether the relationship between the user and the friend isbidirectional (step 3408). In other words, the crowd analyzer 58determines whether the user is also a friend of that friend. If not, theprocess proceeds to step 3412. If so, the crowd analyzer 58 increments abidirectional count (step 3410).

At this point, whether proceeding from step 3404, step 3408, or step3410, the crowd analyzer 58 determines whether the user has more friendsto process (step 3412). If so, the process returns to step 3402 and isrepeated for the next friend of the user. Once all of the friends of theuser have been processed, the crowd analyzer 58 determines whether thereare more users in the crowd fragment (step 3414). If so, the processreturns to step 3400 and is repeated for the next user in the crowdfragment. Once steps 3402 through 3412 have been performed for all ofthe users in the crowd fragment, the crowd analyzer 58 computes a ratioof the bidirectional count (i.e., the number of bidirectional friendrelationships) over the connection count (i.e., the number ofunidirectional and bidirectional friend relationships) for the crowdfragment (step 3416). At this point, the process ends. In thisembodiment, the ratio of the bidirectionality count to the connectioncount reflects the degree of bidirectionality of friendshiprelationships for the crowd fragment and may be returned to therequesting user or subscriber in the crowd data for the correspondingcrowd.

FIGS. 45A through 45C illustrate an exemplary graphical representationof the degree of bidirectionality of friendship relationships for acrowd fragment according to one embodiment of the present disclosure.Note that this graphical representation may also be used to present thedegree of bidirectionality of friendship relationships for a crowd. FIG.45A illustrates the graphical representation for a crowd having a ratioof bidirectional friend relationships to total friend relationships ofapproximately 0.5. FIG. 45B illustrates the graphical representation fora crowd having a ratio of bidirectional friend relationships to totalfriend relationships of approximately 0.2. FIG. 45C illustrates thegraphical representation for a crowd having a ratio of bidirectionalfriend relationships to total friend relationships of approximately0.95. Graphical representations such as those in FIGS. 45A through 45Cmay be presented to the mobile users 20-1 through 20-N by the MAPapplications 32-1 through 32-N or presented to the subscriber 24 by theMAP server 12 via the web browser 38 at the subscriber device 22 basedon data included in the crowd data for corresponding crowds.

FIGS. 46 through 51 describe embodiments of the present disclosure whereconfidence levels for the current locations of users in a crowd aredetermined and utilized to provide a quality level for the aggregateprofile for the crowd and/or confidence levels for individual keywordsincluded in the aggregate profile for the crowd. In general, in manyimplementations, the current locations of the users 20-1 through 20-Nare not updated instantaneously or even substantially instantaneously.There are many reasons why the current locations of the users 20-1through 20-N are not and possibly cannot be updated instantaneously. Forexample, battery life and performance limitations, non-continuousnetwork connectivity, platform limitations such as the inability to runapplications in the background, and security architectures (e.g., J2MEMIDP2.0 security architecture) may all limit the ability of the mobiledevices 18-1 through 18-N to provide continuous location updates to theMAP server 12. As a result, the users 20-1 through 20-N may move fromtheir current locations stored by the MAP server 12 well beforecorresponding location updates are received by the MAP server 12. Forinstance, if the user 20-1 turns the mobile device 18-1 off, then themobile device 18-1 is unable to send location updates for the user 20-1.As such, the current location stored for the user 20-1 at the MAP server12 will no longer be accurate if the user 20-1 moves to a new locationwhile the mobile device 18-1 is off.

FIGS. 46 through 51 describe embodiments where the contribution of theuser profiles of the users 20-1 through 20-N to aggregate profiles ofcorresponding crowds is modified based on an amount of time that hasexpired since receiving location updates for the users 20-1 through20-N. More specifically, FIG. 46 is a flow chart for a process forgenerating a quality level for an aggregate profile for a crowdaccording to one embodiment of the present disclosure. As discussedabove, the crowd analyzer 58 of the MAP server 12 creates an aggregateprofile for one or more crowds relevant to a POI or an AOI in responseto a crowd request from a requestor (i.e., one of the users 20-1 through20-N, the subscriber 24, or the third-party service 26). Depending onthe particular embodiment, the aggregate profile may be generated basedon comparisons of the user profiles of the users in the crowd to a userprofile or a select subset of the user profile of a requesting user(e.g., one of the users 20-1 through 20-N for which the aggregateprofile is generated), comparisons of the user profiles of the users inthe crowd to a target user profile, or comparisons of the user profilesof the users in the crowd to one another. Using the following process,the crowd analyzer 58 can generate a quality level for the aggregateprofile for one or more such crowds. Note that the quality level for theaggregate profile of a crowd may also be viewed as a quality level forthe crowd itself particularly where a spatial crowd formation processhas been used to form the crowd.

First, the crowd analyzer 58 of the MAP server 12 computes confidencelevels for the current locations of the users in the crowd (step 3500).In one embodiment, the confidence level for the current location of auser ranges from 0 to 1, where the confidence level is set to 1 when thecurrent location is updated and then linearly decreases to 0 over somedesired period of time. As such, the confidence level of the currentlocation of a user may be computed based on the following equation:

CL _(LOCATION) =−Δt·DR+CL _(LOCATION,PREVIOUS),

where CL_(LOCATION) is the confidence level of the current location ofthe user, Δt is an amount of time that has elapsed since the confidencelevel of the current location of the user was last computed, DR is apredefined decrease rate or rate at which the confidence level is todecrease over time, and CL_(LOCATION,PREVIOUS) is the previousconfidence level of the current location of the user. The decrease rate(DR) is preferably selected such that the confidence level (CL) of thecurrent location of the user will decrease from 1 to 0 over a desiredamount of time. Note that the decrease rate (DR) may be definedseparately for each user or may be the same for all users. If definedseparately, the decrease rate (DR) for a user may be defined once andre-used or defined on a case-by-case basis based on the user's currentand past locations, profile, history, or the like. The desired amount oftime may be any desired amount of time such as, but not limited to, adesired number of hours. As an example, the desired amount of time maybe 12 hours, and the corresponding decrease rate (DR) is 1/12 if time ismeasured in hours and 1/(12×60×60×1000) if time is measures inmilliseconds. Note that the MAP server 12 stores the confidence level(CL) of the user, a timestamp indicating when the confidence level (CL)was computed, and optionally a timestamp indicating when the currentlocation of the user was last updated. This information may be stored inthe user record for the user. Alternatively, only the timestamp of thelast location update is stored in the user record for the user. If theinitial confidence level (CL) varies per user, the initial confidencelevel (CL) is also stored in the user record. The current confidencelevel (CL) is determined whenever it is needed by retrieving the lastlocation update timestamp from the user record, determining an amount ofelapsed time between the current time and the time of the last locationupdate, and calculating the new confidence level based on the decreaserate (DR) and the initial confidence level (CL). Also note that whilethe confidence levels of the current locations of the users in the crowdare computed using a linear algorithm in the exemplary embodimentdescribed above, nonlinear algorithms may alternatively be used.

When computing the confidence levels for the current locations of theusers in the crowds, the crowd analyzer 58 may also consider locationconfidence events. Note that timestamps of such location confidenceevents and the location confidence events themselves may also be storedto enable correct calculation of the confidence levels. The locationconfidence events may include negative location confidence events suchas, but not limited to, the passing of a known closing time of abusiness (e.g., restaurant, bar, shopping mall, etc.) at which a user islocated or movement of a crowd with which a user has a high affinity.The location confidence events may additionally or alternatively includepositive location confidence events such as, but not limited to,frequent interaction with the corresponding MAP application by the user.Frequent interaction with the MAP application by the user may beindicated by reception of frequent location updates for the user. Notethat, in addition to or as an alternative to using location confidenceevents, other information such as location profiles, event information(e.g., live music event, open-mic night, etc.), current as past crowdhistories, or the like may be used when computing the confidence levelsfor the current locations of the users in the crowds.

The manner in which the crowd analyzer 58 handles positive and/ornegative location confidence events when computing the confidence levelsof the users in the crowd may vary. In one embodiment, in response todetecting a negative location confidence event with respect to a user,the crowd analyzer 58 may increase the decrease rate (DR) used tocompute the confidence level (CL) of the current location of the user.Similarly, in response to detecting a positive location confidence eventwith respect to a user, the crowd analyzer 58 may decrease the decreaserate (DR) used to compute the confidence level (CL) of the currentlocation of the user or replace the decrease rate (DR) with an increaserate such that the confidence level of the user increases in response tothe location confidence event or while the location confidence eventcontinues (e.g., increase while the user frequently interacts with theMAP application).

In another embodiment, in response to detecting a negative locationconfidence event with respect to a user, the crowd analyzer 58 maydecrease the confidence level (CL) of the current location of the userby a predefined amount. For example, if the negative location event isthe passing of a closing time of a business at which the user islocated, the crowd analyzer 58 may decrease the confidence level (CL) ofthe user to zero. Similarly, in response to detecting a positivelocation confidence event with respect to a user, the crowd analyzer 58may increase the confidence level (CL) of the current location of theuser by a predefined amount. For example, in response to detecting thatthe user is frequently interacting with the MAP application at hismobile device, the crowd analyzer 58 may increase the confidence level(CL) of the current location of the user by 0.1.

Once the confidence levels of the current locations of the users in thecrowd are computed, the crowd analyzer 58 determines a quality level forthe aggregate profile of the crowd (step 3502). In one embodiment, thequality level for the crowd is computed as an average of the confidencelevels of the current locations of the users in the crowd. The qualitylevel of the aggregate profile may then be provided along with theaggregate profile in the crowd data for the crowd returned to therequestor.

FIG. 47 illustrates an exemplary GUI 318 for presenting an aggregateprofile 320 for a crowd and a quality level 322 of the aggregate profile320 generated using the process of FIG. 46 according to one embodimentof the present disclosure. FIG. 48 illustrates another exemplary GUI 324for presenting an aggregate profile 326 for a crowd and a quality level328 of the aggregate profile 326 generated using the process of FIG. 46according to one embodiment of the present disclosure. However, in theGUI 324, the aggregate profile 326 also indicates a relative number ofuser matches for each of a number of keywords in the aggregate profile326. More specifically, in a keyword area 330 of the GUI 324, the sizesof the keywords indicate the relative number of user matches for thekeywords. Therefore, in this example, the keyword “books” has a largernumber of user matches that the keyword “politics,” as indicated by thesize, or font size, of the two keywords in the keyword area 330 of theGUI 324.

FIG. 49 illustrates a flow chart for a process for generating confidencefactors for keywords included in an aggregate profile for a crowd basedon confidence levels for current locations of users in the crowdaccording to one embodiment of the present disclosure. As discussedabove, the crowd analyzer 58 creates an aggregate profile for one ormore crowds relevant to a POI or an AOI in response to a crowd requestfrom a requestor (i.e., one of the users 20-1 through 20-N, thesubscriber 24, or the third-party service 26). Depending on theparticular embodiment, the aggregate profile may be generated based oncomparisons of the user profiles of the users in the crowd to a userprofile or a select subset of the user profile of a requesting user(e.g., one of the users 20-1 through 20-N for which the aggregateprofile is generated), comparisons of the user profiles of the users inthe crowd to a target user profile, or comparisons of the user profilesof the users in the crowd to one another. As also discussed above, inone embodiment, the aggregate profile for a crowd includes a number ofuser matches for each of a number of keywords and/or a ratio of thenumber of user matches to the total number of users in the crowd foreach of a number of keywords.

In order to generate confidence factors for each keyword in an aggregateprofile for a crowd, the crowd analyzer 58 of the MAP server 12 computesconfidence levels for the current locations of the users in the crowd(step 3600). The confidence levels for the current locations of theusers may be computed as discussed above with respect to step 3500 ofFIG. 46. In general, the confidence levels for the current locations ofthe users may be computed based on an amount of time since the currentlocation of the user was last updated, location confidence events, orboth. Once the confidence levels of the current locations of the usersin the crowd are computed, the crowd analyzer 58 determines a confidencelevel for each keyword in the aggregate profile of the crowd based onthe confidence levels for the current locations of the correspondingusers (step 3602). In one embodiment, for each keyword, the confidencelevel for the keyword is computed as an average of the confidence levelsof the current locations of the users in the crowd having user profilesincluding the keyword. In other words, for each keyword, there are anumber of user matches. The confidence levels of the current locationsof the users corresponding to the user matches for the keyword areaveraged to provide the confidence level for the keyword.

FIG. 50 illustrates an exemplary GUI 332 for presenting an aggregateprofile 334 for a crowd including an indication of a confidence levelfor each of a number of keywords in the aggregate profile 334 accordingto one embodiment of the present disclosure. More specifically, in thisembodiment, the aggregate profile 334 includes a quality level 336 ofthe aggregate profile 334 generated using the process of FIG. 46.However, the quality level 336 of the aggregate profile 334 is optional.The GUI 332 includes a keyword area 338 that graphically illustrates thekeywords in the aggregate profile 334 and the confidence levels of thekeywords. In this embodiment, the confidence levels of the keywords aregraphically indicated via opacity of the keywords in the keyword area338. The lighter the text of the keyword, the lesser the confidencelevel of the keyword. Conversely, the darker the text of the keyword,the greater the confidence level of the keyword. Thus, in this example,the confidence level for the keyword “books” is greater than theconfidence level of the keyword “politics,” and the confidence level ofthe keyword “photography” is greater than the confidence levels of thekeywords “books” and “politics.” In addition, in this embodiment, thesize of the keywords in the keyword area 338 is indicative of the numberof user matches for the keywords, as discussed above with respect toFIG. 48. Note that in an alternative embodiment, the size of thekeywords in the keyword area 338 may be indicative of the confidencelevels of the keywords rather than the number of user matches for thekeywords.

FIG. 51 graphically illustrates modification of the confidence level ofthe current location of a user according to one embodiment of thepresent disclosure. As illustrated, at time 0, a location update for theuser is received by the MAP server 12 and, as such, the confidence levelof the current location of the user is set to 1. At time 1, a positivelocation confidence event is detected. This positive location confidenceevent may be detected when, for example, the crowd analyzer 58 isgenerating an aggregate profile for a crowd in which the user isincluded and the user has been frequently interacting with the MAPapplication of his mobile device. As a result of the positive locationconfidence event, in this embodiment, the confidence level for thecurrent location of the user at time 1 is computed using an increaserate (i.e., a positive rate of change) rather than a decrease rate (DR).As such, the confidence level of the current location of the userincreases from time 0 to time 1 as shown. Alternatively, in response tothe positive location confidence event, the confidence level for thecurrent location of the user at time 1 may be increased by a predefinedamount such as, for example, 0.1 points. Next, at time 2, anotherpositive location confidence event is detected. As a result of thissecond positive location confidence event, in this embodiment, theincrease rate is further increased, and the confidence level for thecurrent location of the user at time 2 is computed using the newincrease rate. As such, the confidence level of the current location ofthe user further increases from time 1 to time 2. Alternatively, inresponse to the positive location confidence event, the confidence levelfor the current location of the user at time 2 may be further increasedby the predefined amount such as, for example, 0.1 points.

At time 3, the confidence level of the current location of the user isupdated. The confidence level of the current location of the user may beupdated by the crowd analyzer 58 before generating an aggregate profilefor a crowd in which the user is included. In this example, since alocation confidence event is not detected at time 3, the confidencelevel for the current location of the user is computed based on theprevious confidence level computed at time 3 and a predefined decreaserate. As such, the confidence level for the current location of the userat time 3 is less than the confidence level for the current location ofthe user at time 2.

At time 4, a negative location confidence event is detected. As aresult, in this example, the decrease rate is increased, and theconfidence level for the current location of the user at time 4 iscomputed based on the new decrease rate. As such, the confidence levelfor the current location of the user at time 4 is less than theconfidence level for the current location of the user at time 3. Basedon the new decrease rate, the confidence level for the current locationof the user continues to decrease until reaching 0 at approximately 4.5hours after time 0. Alternatively, in response to the negative locationconfidence event, the confidence level for the current location of theuser at time 4 may be decreased by a predefined amount in addition to oras an alternative to decreasing the confidence level by an amountdetermined by the amount of time that has elapsed between time 3 andtime 4 and the decrease rate.

FIG. 52 illustrates the operation of the system 10 of FIG. 1 to performa process for efficiently handling requests for crowd data for largegeographic areas according to one embodiment of the present disclosure.As illustrated, the MAP application 32-1 of the mobile device 18-1 sendsa crowd request to the MAP client 30-1 (step 3700). Next, the MAP client30-1 sends the crowd request to the MAP server 12 (step 3702). Inresponse to receiving the crowd request, the MAP server 12 establishes abounding box for the request (step 3704). Note that while a bounding boxis referred to herein, a bounding region of any desired shape may beused. In one embodiment, the crowd request is a request for crowd datafor a POI such that the bounding box is a geographic region of apredefined size centered at the POI. In another embodiment, the crowdrequest is a request for crowd data for an AOI such that the boundingbox is a geographic region corresponding to the AOI.

The MAP server 12, and more specifically the crowd analyzer 58, thendetermines whether a size of the bounding box is greater than apredefined maximum size (step 3706). While not illustrated, if the sizeof the bounding box is not greater than the predefined maximum size, thecrowd analyzer 58 identifies crowds relevant to the bounding box,obtains crowd data for the crowds, and returns the crowd data to the MAPclient 30-1 in the manner described above. However, in this embodiment,the size of the bounding box is greater than the predefined maximumsize. As such, the crowd analyzer 58 identifies one or more hotspotswithin the bounding box (step 3708). More specifically, the MAP server12 maintains a list of hotspots, and the one or more hotspots within thebounding box are selected from the list of hotspots. In general, ahotspot is a geographic point (e.g., latitude and longitude coordinates,a physical address, or the like) where a significant number of crowdshave historically been located and/or where a significant number ofcrowds are currently located.

In one embodiment, the MAP server 12, and more specifically the crowdanalyzer 58, monitors crowds over time and identifies geographic pointsnear which a significant number of crowds are typically located ashotspots. In another embodiment, hotspots may be defined by the users20-1 through 20-N in a collaborative process. For example, the users20-1 through 20-N may be enabled to nominate geographic points (e.g.,POIs, latitude and longitude coordinates, a street address, or the like)as hotspots. Once a geographic point, or substantially the samegeographic point, receives a predefined minimum number of nominations,the geographic point is defined as a hotspot. The geographic point mayremain a hotspot permanently. Alternatively, the geographic point may beremoved as a hotspot if one or more removal criteria are satisfied suchas, for example, receiving a predefined threshold number of nominationsfor removal as a hotspot over a defined amount of time. In yet anotherembodiment, persons or entities may pay a fee to have desired geographicpoints listed as hotspots. For example, a business owner may pay a feeto have the MAP server 12 list the physical location of his or herbusiness as a hotspot.

Once the hotspots within the bounding box for the request areidentified, the crowd analyzer 58 obtains crowd data for the hotspots(step 3710). More specifically, in one embodiment, the crowd analyzer 58establishes initial request regions of a predefined shape and sizecentered at the hotspots. The initial request regions are preferably anoptimal shape and size. Using the initial request regions centered atthe hotspots, the crowd analyzer 58 identifies crowds relevant to theinitial request regions centered at the hotspots. As discussed above,the crowd analyzer 58 may identify the crowds by performing a spatialcrowd formation process in response to the request. Alternatively, thecrowds may be formed proactively and corresponding crowd records may bestored in the datastore 64 of the MAP server 12. In this case, the crowdanalyzer 58 identifies the crowds relevant to the initial requestregions centered at the hotspots by querying the datastore 64 of the MAPserver 12. The crowd analyzer 58 then obtains crowd data for theidentified crowds. As discussed above, the identified crowds may bepassed to the aggregation engine 60, which may then generate aggregateprofiles for the crowds. In addition or alternatively, the crowdanalyzer 58 may determine characteristics of the crowds such as, forexample, degree of fragmentation, best-case and worst-case average DOS,degree of bidirectionality, or the like.

In addition, the crowd analyzer 58 determines a needed number offollow-up requests to be performed by the MAP client 30-1 in order toobtain crowd data for the rest of the bounding box established for thecrowd request (step 3712). In one embodiment, follow-up requests areused to obtain crowd data for a series of one or more outwardlyradiating, concentric request regions around each of the hotspots. Eachrequest region is a geographic region. Each follow-up request is for acorresponding one of the series of outwardly radiating, concentricrequest regions around the hotspots. The number of needed follow-uprequests depends on the number of hotspots in the bounding box, the sizeof the outwardly radiating, concentric request regions for the follow-uprequests, and the size of the bounding box. The crowd analyzer 58 of theMAP server 12 then sends the crowd data for the hotspots and the needednumber of follow-up requests to the MAP client 30-1 (step 3714). The MAPclient 30-1 then sends the crowd data for the hotspots to the MAPapplication 32-1 (step 3716), and the MAP application 32-1 presents thecrowd data for the hotspots to the user 20-1 (step 3718).

In addition to providing the crowd data for the hotspots to the MAPapplication 32-1, the MAP client 30-1 sends a follow-up request to theMAP server 12 (step 3720). In response, the crowd analyzer 58 of the MAPserver 12 obtains crowd data for the follow-up request (step 3722). Morespecifically, the crowd analyzer 58 identifies the request regions forthe follow-up request. The crowd analyzer 58 then identifies crowdsrelevant to the request regions for the follow-up request and obtainscrowd data for the identified crowds. Note that any redundant crowd datamay be eliminated by carefully structuring the request regions toprevent overlapping of bounding regions from the same follow-up request.Alternatively, either the crowds or the resulting crowd data may befiltered at the MAP server 12 or the MAP client 30-1 to remove redundantcrowds or crowd data. The crowd analyzer 58 of the MAP server 12 thensends the crowd data for the follow-up request to the MAP client 30-1(step 3724). The MAP client 30-1 then sends the crowd data for thefollow-up request to the MAP application 32-1 (step 3726), and the MAPapplication 32-1 presents the crowd data to the user 20-1 (step 3728).

In this embodiment, the needed number of follow-up requests is greaterthan one. As such, the MAP client 30-1 sends a second follow-up requestto the MAP server 12 (step 3730). In response, the crowd analyzer 58 ofthe MAP server 12 obtains crowd data for the second follow-up request(step 3732). More specifically, the crowd analyzer 58 identifies therequest regions for the follow-up request. The crowd analyzer 58 thenidentifies crowds that are relevant to the request regions for thefollow-up request and obtains crowd data for the identified crowds.Again, note that any redundant crowd data may be eliminated by carefullystructuring the request regions to prevent overlapping of requestregions from the same follow-up request or previous follow-up requests.Alternatively, either the crowds or the resulting crowd data may befiltered at the MAP server 12 or the MAP client 30-1 to remove redundantcrowds or crowd data. The crowd analyzer 58 of the MAP server 12 thensends the crowd data for the follow-up request to the MAP client 30-1(step 3734). The MAP client 30-1 then sends the crowd data for thefollow-up request to the MAP application 32-1 (step 3736), and the MAPapplication 32-1 presents the crowd data to the user 20-1 (step 3738).This process continues until crowd data for all of the follow-uprequests has been obtained or until the process is otherwise terminated.For example, the process may be otherwise terminated if the user 20-1initiates a crowd request for a different POI or AOI, if the user 20-1deactivates the MAP application 32-1, or the like.

FIGS. 53A through 53E illustrate an exemplary series of outwardlyradiating, concentric geographic regions for a number of hotspots HS1through HS3 identified for a bounding box 340 established by the MAPserver 12 in response to a crowd request. FIG. 53A illustrates thebounding box 340 established for the request and the hotspots HS1through HS3 identified within the bounding box 340. FIG. 53B illustratesinitial request regions R0 ₁ through R0 ₃ established for the hotspotsHS1 through HS3, respectively. As discussed above, the crowd analyzer 58identifies crowds relevant to the initial request regions R0 ₁ throughR0 ₃, obtains crowd data for the identified crowds, and returns thecrowd data to the MAP client 30-1. In addition, the crowd analyzer 58determines the needed number of follow-up requests for the bounding box340. The needed number of follow-up requests for the bounding box 340may vary depending on the size of the bounding box 340, the size of theinitial and follow-up request regions, and the number of hotspots in thebounding box 340. In this example, the needed number of follow-uprequests is nine.

FIG. 53C illustrates first request regions R1 ₁ through R1 ₃ for thehotspots HS1 through HS3, respectively, for a first follow-up request.Note that, in this exemplary example, a portion of the first requestregion R1 ₃ for the third hotspot HS3 overlaps the first request regionR1 ₂ for the second hotspot HS2. Thus, in order to prevent duplicate, orredundant, crowds and crowd data, filtering may be applied.Alternatively, the first request region R1 ₃ of the third hotspot HS3may be modified to exclude the portion that overlaps the first requestregion R1 ₂ of the second hotspot HS2. This redundancy may alternativelybe addressed by querying the datastore 64 with a mathematical union ofthe first request regions R1 ₁ through R1 ₃. When the crowd analyzer 58receives the first follow-up request, the crowd analyzer 58 identifiescrowds relevant to the first request regions R1 ₁ through R1 ₃, obtainscrowd data for the identified crowds, and returns the crowd data to theMAP client 30-1.

FIG. 53D illustrates second request regions R2 ₁ through R2 ₃ for thehotspots HS1 through HS3, respectively, for a second follow-up request.Note that, in this exemplary embodiment, the second request region R2 ₂of the second hotspot HS2 overlaps the second request region R2 ₁ of thefirst hotspot HS1 and the second request region R2 ₃ of the thirdhotspot HS3 overlaps the initial, first, and second request regions R0₂, R1 ₂, and R2 ₂ of the second hotspot HS2. Thus, in order to preventduplicate, or redundant, crowds and crowd data, filtering may beapplied. Alternatively, the second request region R2 ₂ of the secondhotspot HS2 may be modified to exclude the portion that overlaps thesecond request region R2 ₁ of the first hotspot HS1. Likewise, thesecond request region R2 ₃ of the third hotspot HS3 may be modified toexclude the portion that overlaps the initial, first, and second requestregions R0 ₂, R1 ₂, and R2 ₂ of the second hotspot HS2. This redundancymay alternatively be addressed by querying the datastore 64 with amathematical union of the second request regions R2 ₁ through R2 ₃ andthen filtering to remove crowds that are duplicates from previousqueries made to the datastore 64 for the initial and first requestregions R0 ₁ through R0 ₃ and R1 ₁ through R1 ₃. When the crowd analyzer58 receives the second follow-up request, the crowd analyzer 58identifies crowds relevant to the second request regions R2 ₁ through R2₃, obtains crowd data for the identified crowds, and returns the crowddata to the MAP client 30-1. This process continues for a number ofadditional follow-up requests until the crowd data is returned for allof the bounding box 340, as illustrated in FIG. 53E. Note that in FIG.53E, dashed lines for overlapping request regions have been omitted forclarity.

FIG. 54 graphically illustrates one exemplary variation to the follow-uprequest regions illustrated in FIGS. 53A through 53E. In thisembodiment, once there is a substantial amount of overlap between thefollow-up regions for the hotspots HS1 through HS3, the crowd analyzer58 may establish follow-up regions that are no longer outwardlyradiating, concentric regions around the hotspots HS1 through HS3. Morespecifically, in this example, the second request regions R2 ₁ throughR2 ₃ for the second follow-up request have a substantial amount ofoverlap. As such, request regions R3 through R6 for subsequent follow-uprequests are provided such that the remaining portion of the boundingbox 340 is quickly and efficiently filled-in. Sizes of the requestregions R3 through R6 are preferably an optimal size or approximately anoptimal size for querying the datastore 64 of the MAP server 12 forrelevant crowds.

The discussion above with respect to FIGS. 52 through 54 provides aprocess for handling a request for crowd data for a large geographicarea by first focusing on hotspots within the large geographic area andthen progressing outwardly from those hotspots to progressively providecrowd data for the large geographic area to the requestor. In anotherembodiment, a request for a large geographic area may be handled byfirst focusing on locations within the large geographic area such aslocations of friends of the requestor and/or POIs previously defined orselected by the requestor and then progressing outwardly from thoselocations until crowd data for the large geographic area is returned tothe requestor. These other locations may be used in addition to or as analternative to hotspots. These other locations may be used in the samemanner described above with respect to hotspots in order to divide arequest for a large geographic area into an initial request and a numberof follow-up requests.

In yet another embodiment, when a request for crowd data for a largegeographic area is received by the MAP server 12, crowds within thelarge geographic area may be identified and corresponding crowd data isobtained. The MAP server 12 may then first return the crowd data forcrowds satisfying predefined criteria. For example, the MAP server 12may return the crowd data for the crowds according to match strengthbetween the user profiles of the users in the crowd and the user profileof the requesting user, a select portion of the user profile of therequesting user, or a target profile defined or otherwise specified bythe requesting user. In this manner, the most relevant crowd data may bereturned to the requesting user first.

It should be noted that while the process described above with respectto FIGS. 52 through 54 focuses on a request from one of the mobiledevices 18-1 through 18-N, a similar process may be used internally atthe MAP server 12 to process requests for crowd data for largegeographic areas from the subscriber device 22 and/or the third-partyservice 26. For example, upon receiving a request for crowd data for alarge geographic area from the subscriber device 22 via the web browser38, the MAP server 12 may first obtain and return crowd data for one ormore hotspots within the large geographic area and then progressivelyreturn crowd data for outwardly radiating, concentric areas around thehotspots.

FIGS. 55 through 61 describe aspects of an embodiment of the presentdisclosure wherein the crowd analyzer 58 of the MAP server 12 provides acrowd tracking feature. In general, over time, the crowd analyzer 58creates a number of crowd snapshots for each crowd. In addition, inorder to accurately track the crowds, the crowd analyzer 58 capturescrowd mergers, captures crowd splits, and re-establishes crowds, asdiscussed below in detail.

FIG. 55 illustrates exemplary data records that may be used to representcrowds, users, crowd snapshots, and anonymous users according to oneembodiment of the present disclosure. As illustrated, for each crowdcreated by the crowd analyzer 58 of the MAP server 12 (i.e., each crowdcreated that has three or more users), a corresponding crowd record 342is created and stored in the datastore 64 of the MAP server 12. Thecrowd record 342 for a crowd includes a users field, a North-East (NE)corner field, a South-West (SW) corner field, a center field, a crowdsnapshots field, a split from field, and a combined into field. Theusers field stores a set or list of user records 344 corresponding to asubset of the users 20-1 through 20-N that are currently in the crowd.The NE corner field stores a location corresponding to a NE corner of abounding box for the crowd. The NE corner may be defined by latitude andlongitude coordinates and optionally an altitude. Similarly, the SWcorner field stores a location of a SW corner of the bounding box forthe crowd. Like the NE corner, the SW corner may be defined by latitudeand longitude coordinates and optionally an altitude. Together, the NEcorner and the SW corner define a bounding box for the crowd, where theedges of the bounding box pass through the current locations of theoutermost users in the crowd. The center field stores a locationcorresponding to a center of the crowd. The center of the crowd may bedefined by latitude and longitude coordinates and optionally analtitude. Together, the NE corner, the SW corner, and the center of thecrowd form spatial information defining the location of the crowd. Note,however, that the spatial information defining the location of the crowdmay include additional or alternative information depending on theparticular implementation. The crowd snapshots field stores a list ofcrowd snapshot records 346 corresponding to crowd snapshots for thecrowd. As discussed below in detail, the split from field may be used tostore a reference to a crowd record corresponding to another crowd fromwhich the crowd split, and the combined into field may be used to storea reference to a crowd record corresponding to another crowd into whichthe crowd has been merged.

Each of the user records 344 includes an ID field, a location field, aprofile field, a crowd field, and a previous crowd field. The ID fieldstores a unique ID for one of the users 20-1 through 20-N for which theuser record 344 is stored. The location field stores the currentlocation of the user, which may be defined by latitude and longitudecoordinates and optionally an altitude. The profile field stores theuser profile of the user, which may be defined as a list of keywords forone or more profile categories. The crowd field is used to store areference to a crowd record of a crowd of which the user is currently amember. The previous crowd field may be used to store a reference to acrowd record of a crowd of which the user was previously a member.

Each of the crowd snapshot records 346 includes an anonymous usersfield, a NE corner field, a SW corner field, a center field, a sampletime field, and a vertices field. The anonymous users field stores a setor list of anonymous user records 348, which are anonymized versions ofuser records for the users that are in the crowd at a time the crowdsnapshot was created. The NE corner field stores a locationcorresponding to a NE corner of a bounding box for the crowd at the timethe crowd snapshot was created. The NE corner may be defined by latitudeand longitude coordinates and optionally an altitude. Similarly, the SWcorner field stores a location of a SW corner of the bounding box forthe crowd at the time the crowd snapshot was created. Like the NEcorner, the SW corner may be defined by latitude and longitudecoordinates and optionally an altitude. The center field stores alocation corresponding to a center of the crowd at the time the crowdsnapshot was created. The center of the crowd may be defined by latitudeand longitude coordinates and optionally an altitude. Together, the NEcorner, the SW corner, and the center of the crowd form spatialinformation defining the location of the crowd at the time the crowdsnapshot was created. Note, however, that the spatial informationdefining the location of the crowd at the time the crowd snapshot wascreated may include additional or alternative information depending onthe particular implementation. The sample time field stores a timestampindicating a time at which the crowd snapshot was created. The timestamppreferably includes a date and a time of day at which the crowd snapshotwas created. The vertices field stores locations of users in the crowdat the time the crowd snapshot was created that define an actual outerboundary of the crowd (e.g., as a polygon) at the time the crowdsnapshot was created. Note that the actual outer boundary of a crowd maybe used to show the location of the crowd when displayed to a user.

Each of the anonymous user records 348 includes an anonymous ID fieldand a profile field. The anonymous ID field stores an anonymous user ID,which is preferably a unique user ID that is not tied, or linked, backto any of the users 20-1 through 20-N and particularly not tied back tothe user or the user record for which the anonymous user record 348 hasbeen created. In one embodiment, the anonymous user records 348 for acrowd snapshot record 346 are anonymized versions of the user records344 of the users in the crowd at the time the crowd snapshot wascreated. The manner in which the user records 344 are anonymized tocreate the anonymous user records 348 may be the same as that describedabove with respect to maintaining a historical record of anonymized userprofile data according to location. The profile field stores theanonymized user profile of the anonymous user, which may be defined as alist of keywords for one or more profile categories.

FIGS. 56A through 56D illustrate one embodiment of a spatial crowdformation process that may be used to enable the crowd tracking feature.This spatial crowd formation process is similar to that described abovewith respect to FIGS. 24A through 24D. In this embodiment, the spatialcrowd formation process is triggered in response to receiving a locationupdate for one of the users 20-1 through 20-N and is preferably repeatedfor each location update received for the users 20-1 through 20-N. Assuch, first, the crowd analyzer 58 receives a location update, or a newlocation, for a user (step 3800). In response, the crowd analyzer 58retrieves an old location of the user, if any (step 3802). The oldlocation is the current location of the user prior to receiving the newlocation of the user. The crowd analyzer 58 then creates a new boundingbox of a predetermined size centered at the new location of the user(step 3804) and an old bounding box of a predetermined size centered atthe old location of the user, if any (step 3806). The predetermined sizeof the new and old bounding boxes may be any desired size. As oneexample, the predetermined size of the new and old bounding boxes is 40meters by 40 meters. Note that if the user does not have an old location(i.e., the location received in step 3800 is the first location receivedfor the user), then the old bounding box is essentially null. Also notethat while bounding “boxes” are used in this example, the boundingregions may be of any desired shape.

Next, the crowd analyzer 58 determines whether the new and old boundingboxes overlap (step 3808). If so, the crowd analyzer 58 creates abounding box encompassing the new and old bounding boxes (step 3810).For example, if the new and old bounding boxes are 40×40 meter regionsand a 1×1 meter square at the northeast corner of the new bounding boxoverlaps a 1×1 meter square at the southwest corner of the old boundingbox, the crowd analyzer 58 may create a 79×79 meter square bounding boxencompassing both the new and old bounding boxes.

The crowd analyzer 58 then determines the individual users and crowdsrelevant to the bounding box created in step 3810 (step 3812). Note thatthe crowds relevant to the bounding box are pre-existing crowdsresulting from previous iterations of the spatial crowd formationprocess. In this embodiment, the crowds relevant to the bounding box arecrowds having crowd bounding boxes that are within or overlap thebounding box established in step 3810. In order to determine therelevant crowds, the crowd analyzer 58 queries the datastore 64 of theMAP server 12 to obtain crowd records for crowds that are within oroverlap the bounding box established in step 3810. The individual usersrelevant to the bounding box are users that are currently located withinthe bounding box and are not already members of a crowd. In order toidentify the relevant individual users, the crowd analyzer 58 queriesthe datastore 64 of the MAP server 12 for user records of users that arecurrently located in the bounding box created in step 3810 and are notalready members of a crowd. Next, the crowd analyzer 58 computes anoptimal inclusion distance for individual users based on user densitywithin the bounding box (step 3814). The optimal inclusion distance maybe computed as described above with respect to step 2314 of FIG. 24A.

The crowd analyzer 58 then creates a crowd of one user for eachindividual user within the bounding box established in step 3810 that isnot already included in a crowd and sets the optimal inclusion distancefor those crowds to the initial optimal inclusion distance (step 3816).The crowds created for the individual users are temporary crowds createdfor purposes of performing the crowd formation process. At this point,the process proceeds to FIG. 56B where the crowd analyzer 58 analyzesthe crowds in the bounding box established in step 3810 to determinewhether any of the crowd members (i.e., users in the crowds) violate theoptimal inclusion distance of their crowds (step 3818). Any crowd memberthat violates the optimal inclusion distance of his or her crowd is thenremoved from that crowd and the previous crowd fields in thecorresponding user records are set (step 3820). More specifically, inthis embodiment, a member is removed from a crowd by removing the userrecord of the member from the set or list of user records in the crowdrecord of the crowd and setting the previous crowd stored in the userrecord of the user to the crowd from which the member has been removed.The crowd analyzer 58 then creates a crowd of one user for each of theusers removed from their crowds in step 3820 and sets the optimalinclusion distance for the newly created crowds to the initial optimalinclusion distance (step 3822).

Next, the crowd analyzer 58 determines the two closest crowds in thebounding box (step 3824) and a distance between the two closest crowds(step 3826). The distance between the two closest crowds is the distancebetween the crowd centers of the two closest crowds, which are stored inthe crowd records for the two closest crowds. The crowd analyzer 58 thendetermines whether the distance between the two closest crowds is lessthan the optimal inclusion distance of a larger of the two closestcrowds (step 3828). If the two closest crowds are of the same size(i.e., have the same number of users), then the optimal inclusiondistance of either of the two closest crowds may be used. Alternatively,if the two closest crowds are of the same size, the optimal inclusiondistances of both of the two closest crowds may be used such that thecrowd analyzer 58 determines whether the distance between the twoclosest crowds is less than the optimal inclusion distances of both ofthe crowds. As another alternative, if the two closest crowds are of thesame size, the crowd analyzer 58 may compare the distance between thetwo closest crowds to an average of the optimal inclusion distances ofthe two crowds.

If the distance between the two closest crowds is greater than theoptimal inclusion distance, the process proceeds to step 3840. However,if the distance between the two closest crowds is less than the optimalinclusion distance, the two crowds are merged (step 3830). The manner inwhich the two crowds are merged differs depending on whether the twocrowds are pre-existing crowds or temporary crowds created for thespatial crowd formation process. If both crowds are pre-existing crowds,one of the two crowds is selected as a non-surviving crowd and the otheris selected as a surviving crowd. If one crowd is larger than the other,the smaller crowd is selected as the non-surviving crowd and the largercrowd is selected as a surviving crowd. If the two crowds are of thesame size, one of the crowds is selected as the surviving crowd and theother crowd is selected as the non-surviving crowd using any desiredtechnique. The non-surviving crowd is then merged into the survivingcrowd by adding the set or list of user records for the non-survivingcrowd to the set or list of user records for the surviving crowd andsetting the merged into field of the non-surviving crowd to a referenceto the crowd record of the surviving crowd. In addition, the crowdanalyzer 58 sets the previous crowd fields of the user records in theset or list of user records from the non-surviving crowd to a referenceto the crowd record of the non-surviving crowd.

If one of the crowds is a temporary crowd and the other crowd is apre-existing crowd, the temporary crowd is selected as the non-survivingcrowd, and the pre-existing crowd is selected as the surviving crowd.The non-surviving crowd is then merged into the surviving crowd byadding the set or list of user records from the crowd record of thenon-surviving crowd to the set or list of user records in the crowdrecord of the surviving crowd. However, since the non-surviving crowd isa temporary crowd, the previous crowd field(s) of the user record(s) ofthe user(s) in the non-surviving crowd are not set to a reference to thecrowd record of the non-surviving crowd. Similarly, the crowd record ofthe temporary record may not have a merged into field, but, if it does,the merged into field is not set to a reference to the surviving crowd.

If both the crowds are temporary crowds, one of the two crowds isselected as a non-surviving crowd and the other is selected as asurviving crowd. If one crowd is larger than the other, the smallercrowd is selected as the non-surviving crowd and the larger crowd isselected as a surviving crowd. If the two crowds are of the same size,one of the crowds is selected as the surviving crowd and the other crowdis selected as the non-surviving crowd using any desired technique. Thenon-surviving crowd is then merged into the surviving crowd by addingthe set or list of user records for the non-surviving crowd to the setor list of user records for the surviving crowd. However, since thenon-surviving crowd is a temporary crowd, the previous crowd field(s) ofthe user record(s) of the user(s) in the non-surviving crowd are not setto a reference to the crowd record of the non-surviving crowd.Similarly, the crowd record of the temporary record may not have amerged into field, but, if it does, the merged into field is not set toa reference to the surviving crowd.

Next, the crowd analyzer 58 removes the non-surviving crowd (step 3832).In this embodiment, the manner in which the non-surviving crowd isremoved depends on whether the non-surviving crowd is a pre-existingcrowd or a temporary crowd. If the non-surviving crowd is a pre-existingcrowd, the removal process is performed by removing or nulling the usersfield, the NE corner field, the SW corner field, and the center field ofthe crowd record of the non-surviving crowd. In this manner, the spatialinformation for the non-surviving crowd is removed from thecorresponding crowd record such that the non-surviving or removed crowdwill no longer be found in response to spatial-based queries on thedatastore 64. However, the crowd snapshots for the non-surviving crowdare still available via the crowd record for the non-surviving crowd. Incontrast, if the non-surviving crowd is a temporary crowd, the crowdanalyzer 58 may remove the crowd by deleting the corresponding crowdrecord.

The crowd analyzer 58 also computes a new crowd center for the survivingcrowd (step 3834). Again, a center of mass algorithm may be used tocompute the crowd center of a crowd. In addition, a new optimalinclusion distance for the surviving crowd is computed (step 3836). Inone embodiment, the new optimal inclusion distance for the survivingcrowd is computed in the manner described above with respect to step2334 of FIG. 24B.

At this point, the crowd analyzer 58 determines whether a maximum numberof iterations have been performed (step 3838). The maximum number ofiterations is a predefined number that ensures that the crowd formationprocess does not indefinitely loop over steps 3818 through 3836 or loopover steps 3818 through 3836 more than a desired maximum number oftimes. If the maximum number of iterations has not been reached, theprocess returns to step 3818 and is repeated until either the distancebetween the two closest crowds is not less than the optimal inclusiondistance of the larger crowd or the maximum number of iterations hasbeen reached. At that point, the crowd analyzer 58 removes crowds withless than three users, or members (step 3840) and the process ends. Asdiscussed above, in this embodiment, the manner in which a crowd isremoved depends on whether the crowd is a pre-existing crowd or atemporary crowd. If the crowd is a pre-existing crowd, a removal processis performed by removing or nulling the users field, the NE cornerfield, the SW corner field, and the center field of the crowd record ofthe crowd. In this manner, the spatial information for the crowd isremoved from the corresponding crowd record such that the crowd will nolonger be found in response to spatial-based queries on the datastore64. However, the crowd snapshots for the crowd are still available viathe crowd record for the crowd. In contrast, if the crowd is a temporarycrowd, the crowd analyzer 58 may remove the crowd by deleting thecorresponding crowd record. In this manner, crowds having less thanthree members are removed in order to maintain privacy of individuals aswell as groups of two users (e.g., a couple).

Returning to step 3808 in FIG. 56A, if the new and old bounding boxes donot overlap, the process proceeds to FIG. 56C and the bounding box to beprocessed is set to the old bounding box (step 3842). In general, thecrowd analyzer 58 then processes the old bounding box in much that samemanner as described above with respect to steps 3812 through 3840. Morespecifically, the crowd analyzer 58 determines the individual users andcrowds relevant to the bounding box (step 3844). Again, note that thecrowds relevant to the bounding box are pre-existing crowds resultingfrom previous iterations of the spatial crowd formation process. In thisembodiment, the crowds relevant to the bounding box are crowds havingcrowd bounding boxes that are within or overlap the bounding box. Theindividual users relevant to the bounding box are users that arecurrently located within the bounding box and are not already members ofa crowd. Next, the crowd analyzer 58 computes an optimal inclusiondistance for individual users based on user density within the boundingbox (step 3846). The optimal inclusion distance may be computed asdescribed above with respect to step 2344 of FIG. 24C.

The crowd analyzer 58 then creates a crowd of one user for eachindividual user within the bounding box that is not already included ina crowd and sets the optimal inclusion distance for the crowds to theinitial optimal inclusion distance (step 3848). The crowds created forthe individual users are temporary crowds created for purposes ofperforming the crowd formation process. At this point, the crowdanalyzer 58 analyzes the crowds in the bounding box to determine whetherany crowd members (i.e., users in the crowds) violate the optimalinclusion distance of their crowds (step 3850). Any crowd member thatviolates the optimal inclusion distance of his or her crowd is thenremoved from that crowd and the previous crowd fields in thecorresponding user records are set (step 3852). More specifically, inthis embodiment, a member is removed from a crowd by removing the userrecord of the member from the set or list of user records in the crowdrecord of the crowd and setting the previous crowd stored in the userrecord of the user to the crowd from which the member has been removed.The crowd analyzer 58 then creates a crowd for each of the users removedfrom their crowds in step 3852 and sets the optimal inclusion distancefor the newly created crowds to the initial optimal inclusion distance(step 3854).

Next, the crowd analyzer 58 determines the two closest crowds in thebounding box (step 3856) and a distance between the two closest crowds(step 3858). The distance between the two closest crowds is the distancebetween the crowd centers of the two closest crowds. The crowd analyzer58 then determines whether the distance between the two closest crowdsis less than the optimal inclusion distance of a larger of the twoclosest crowds (step 3860). If the two closest crowds are of the samesize (i.e., have the same number of users), then the optimal inclusiondistance of either of the two closest crowds may be used. Alternatively,if the two closest crowds are of the same size, the optimal inclusiondistances of both of the two closest crowds may be used such that thecrowd analyzer 58 determines whether the distance between the twoclosest crowds is less than the optimal inclusion distances of both ofthe two closest crowds. As another alternative, if the two closestcrowds are of the same size, the crowd analyzer 58 may compare thedistance between the two closest crowds to an average of the optimalinclusion distances of the two closest crowds.

If the distance between the two closest crowds is greater than theoptimal inclusion distance, the process proceeds to step 3872. However,if the distance between the two closest crowds is less than the optimalinclusion distance, the two crowds are merged (step 3862). The manner inwhich the two crowds are merged differs depending on whether the twocrowds are pre-existing crowds or temporary crowds created for thespatial crowd formation process. If both crowds are pre-existing crowds,one of the two crowds is selected as a non-surviving crowd and the otheris selected as a surviving crowd. If one crowd is larger than the other,the smaller crowd is selected as the non-surviving crowd and the largercrowd is selected as a surviving crowd. If the two crowds are of thesame size, one of the crowds is selected as the surviving crowd and theother crowd is selected as the non-surviving crowd using any desiredtechnique. The non-surviving crowd is then merged into the survivingcrowd by adding the set or list of user records for the non-survivingcrowd to the set or list of user records for the surviving crowd andsetting the merged into field of the non-surviving crowd to a referenceto the crowd record of the surviving crowd. In addition, the crowdanalyzer 58 sets the previous crowd fields of the set or list of userrecords from the non-surviving crowd to a reference to the crowd recordof the non-surviving crowd.

If one of the crowds is a temporary crowd and the other crowd is apre-existing crowd, the temporary crowd is selected as the non-survivingcrowd, and the pre-existing crowd is selected as the surviving crowd.The non-surviving crowd is then merged into the surviving crowd byadding the user records from the set or list of user records from thecrowd record of the non-surviving crowd to the set or list of userrecords in the crowd record of the surviving crowd. However, since thenon-surviving crowd is a temporary crowd, the previous crowd field(s) ofthe user record(s) of the user(s) in the non-surviving crowd are not setto a reference to the crowd record of the non-surviving crowd.Similarly, the crowd record of the temporary record may not have amerged into field, but, if it does, the merged into field is not set toa reference to the surviving crowd.

If both the crowds are temporary crowds, one of the two crowds isselected as a non-surviving crowd and the other is selected as asurviving crowd. If one crowd is larger than the other, the smallercrowd is selected as the non-surviving crowd and the larger crowd isselected as a surviving crowd. If the two crowds are of the same size,one of the crowds is selected as the surviving crowd and the other crowdis selected as the non-surviving crowd using any desired technique. Thenon-surviving crowd is then merged into the surviving crowd by addingthe set or list of user records for the non-surviving crowd to the setor list of user records for the surviving crowd. However, since thenon-surviving crowd is a temporary crowd, the previous crowd field(s) ofthe user record(s) of the user(s) in the non-surviving crowd are not setto a reference to the crowd record of the non-surviving crowd.Similarly, the crowd record of the temporary record may not have amerged into field, but, if it does, the merged into field is not set toa reference to the surviving crowd.

Next, the crowd analyzer 58 removes the non-surviving crowd (step 3864).In this embodiment, the manner in which the non-surviving crowd isremoved depends on whether the non-surviving crowd is a pre-existingcrowd or a temporary crowd. If the non-surviving crowd is a pre-existingcrowd, the removal process is performed by removing or nulling the usersfield, the NE corner field, the SW corner field, and the center field ofthe crowd record of the non-surviving crowd. In this manner, the spatialinformation for the non-surviving crowd is removed from thecorresponding crowd record such that the non-surviving or removed crowdwill no longer be found in response to spatial-based queries on thedatastore 64. However, the crowd snapshots for the non-surviving crowdare still available via the crowd record for the non-surviving crowd. Incontrast, if the non-surviving crowd is a temporary crowd, the crowdanalyzer 58 may remove the crowd by deleting the corresponding crowdrecord.

The crowd analyzer 58 also computes a new crowd center for the survivingcrowd (step 3866). Again, a center of mass algorithm may be used tocompute the crowd center of a crowd. In addition, a new optimalinclusion distance for the surviving crowd is computed (step 3868). Inone embodiment, the new optimal inclusion distance for the survivingcrowd is computed in the manner described above with respect to step2364 of FIG. 24D.

At this point, the crowd analyzer 58 determines whether a maximum numberof iterations have been performed (step 3870). If the maximum number ofiterations has not been reached, the process returns to step 3850 and isrepeated until either the distance between the two closest crowds is notless than the optimal inclusion distance of the larger crowd or themaximum number of iterations has been reached. At that point, the crowdanalyzer 58 removes crowds with less than three users, or members (step3872). As discussed above, in this embodiment, the manner in which acrowd is removed depends on whether the crowd is a pre-existing crowd ora temporary crowd. If the crowd is a pre-existing crowd, a removalprocess is performed by removing or nulling the users field, the NEcorner field, the SW corner field, and the center field of the crowdrecord of the crowd. In this manner, the spatial information for thecrowd is removed from the corresponding crowd record such that the crowdwill no longer be found in response to spatial-based queries on thedatastore 64. However, the crowd snapshots for the crowd are stillavailable via the crowd record for the crowd. In contrast, if the crowdis a temporary crowd, the crowd analyzer 58 may remove the crowd bydeleting the corresponding crowd record. In this manner, crowds havingless than three members are removed in order to maintain privacy ofindividuals as well as groups of two users (e.g., a couple).

The crowd analyzer 58 then determines whether the crowd formationprocess for the new and old bounding boxes is done (step 3874). In otherwords, the crowd analyzer 58 determines whether both the new and oldbounding boxes have been processed. If not, the bounding box is set tothe new bounding box (step 3876), and the process returns to step 3844and is repeated for the new bounding box. Once both the new and oldbounding boxes have been processed, the crowd formation process ends.

FIG. 57 illustrates a process for creating crowd snapshots according toone embodiment of the present disclosure. In this embodiment, after thespatial crowd formation process of FIGS. 56A through 56D is performed inresponse to a location update for a user, the crowd analyzer 58 detectscrowd change events, if any, for the relevant crowds (step 3900). Therelevant crowds are pre-existing crowds that are within the boundingregion(s) processed during the spatial crowd formation process inresponse to the location update for the user. The crowd analyzer 58 maydetect crowd change events by comparing the crowd records of therelevant crowds before and after performing the spatial crowd formationprocess in response to the location update for the user. The crowdchange events may be a change in the users in the crowd, a change to alocation of one of the users within the crowd, or a change in thespatial information for the crowd (e.g., the NE corner, the SW corner,or the crowd center). Note that if multiple crowd change events aredetected for a single crowd, then those crowd change events arepreferably consolidated into a single crowd change event.

Next, the crowd analyzer 58 determines whether there are any crowdchange events (step 3902). If not, the process ends. Otherwise, thecrowd analyzer 58 gets the next crowd change event (step 3904) andgenerates a crowd snapshot for a corresponding crowd (step 3906). Morespecifically, the crowd change event identifies a crowd record storedfor a crowd for which the crowd change event was detected. A crowdsnapshot is then created for that crowd by creating a new crowd snapshotrecord for the crowd and adding the new crowd snapshot to the list ofcrowd snapshots stored in the crowd record for the crowd. The crowdsnapshot record includes a set or list of anonymized user records, whichare an anonymized version of the user records for the users in the crowdat the current time. In addition, the crowd snapshot record includes theNE corner, the SW corner, and the center of the crowd at the currenttime as well as a timestamp defining the current time as the sample timeat which the crowd snapshot record was created. Lastly, locations ofusers in the crowd that define the outer boundary of the crowd at thecurrent time are stored in the crowd snapshot record as the vertices ofthe crowd. After creating the crowd snapshot, the crowd analyzer 58determines whether there are any more crowd change events (step 3908).If so, the process returns to step 3904 and is repeated for the nextcrowd change event. Once all of the crowd change events are processed,the process ends.

FIG. 58 illustrates a process that may be used to re-establish crowdsand detect crowd splits according to one embodiment of the presentdisclosure. In general, in order to accurately track a crowd, it ispreferable to enable crowds that have been removed to be re-establishedin the future. For example, a crowd may be removed as a result of usersin the crowd deactivating their MAP applications (or powering down theirmobile devices). If those users then move together to a differentlocation and then reactivate their MAP applications (or power on theirmobile devices), it is preferable for the resulting crowd to beidentified as the same crowd that was previously removed. In otherwords, it is desirable to re-establish the crowd. In addition, in orderto accurately track a crowd, it is desirable to capture when the crowdsplits into two or more crowds.

Accordingly, in this embodiment, the spatial crowd formation process ofFIGS. 56A through 56D is performed in response to a location update fora user. The crowd analyzer 58 then gets a next relevant crowd (step4000). The relevant crowds are pre-existing and new crowds that arewithin the bounding region(s) processed during the spatial crowdformation process in response to the location update for the user. Notethat, for the first iteration, the next relevant crowd is the firstrelevant crowd. The crowd analyzer 58 then determines a maximum numberof users in the crowd from a common previous crowd (step 4002). Morespecifically, the crowd analyzer 58 examines the previous crowd fieldsof the user records of all of the users in the crowd to identify usersfrom a common previous crowd. For each previous crowd found in the userrecords of the users in the crowd, the crowd analyzer 58 counts thenumber of users in the crowd that are from that previous crowd. Thecrowd analyzer 58 then selects the previous crowd having the highestnumber of users, and determines that the number of users counted for theselected previous crowd is the maximum number of users in the crowd froma common previous crowd.

The crowd analyzer 58 then determines whether the maximum number ofusers in the crowd from a common previous crowd is greater than apredefined threshold number of users (step 4004). In an alternativeembodiment, rather than determining the maximum number of users from acommon previous crowd and comparing that number to a predefinedthreshold number of users, a maximum percentage of users in the crowdfrom a common previous crowd may be determined and compared to apredefined threshold percentage. If the maximum number of users in thecrowd from a common previous crowd is not greater than the predefinedthreshold number of users, the process proceeds to step 4010. Otherwise,the crowd analyzer 58 determines whether the common previous crowd hasbeen removed (step 4006). If so, then the crowd is re-established as thecommon previous crowd (step 4008). More specifically, in thisembodiment, the crowd is re-established as the common previous crowd bystoring the set or list of user records, the NE corner, the SW corner,and the center from the crowd record of the crowd in the crowd record ofthe common previous crowd. The crowd record for the crowd may thendeleted. In addition, the previous crowd fields of the users from thecommon previous crowd may be set to null or otherwise cleared. Once thecommon previous crowd is re-established, the crowd analyzer 58determines whether there are more relevant crowds to process (step4010). If so, the process returns to step 4000 and is repeated until allrelevant crowds are processed.

Returning to step 4006, if the common previous crowd has not beenremoved, the crowd analyzer 58 identifies the crowd as being split fromthe common previous crowd (step 4012). More specifically, in thisembodiment, the crowd analyzer 58 stores a reference to the crowd recordof the common previous crowd in the split from field of the crowd recordof the crowd. At this point, the crowd analyzer 58 then determineswhether there are more relevant crowds to process (step 4010). If so,the process returns to step 4000 and is repeated until all relevantcrowds are processed, at which time the process ends.

FIG. 59 graphically illustrates the process of re-establishing a crowdfor an exemplary crowd according to one embodiment of the presentdisclosure. As illustrated, at TIME 1, CROWD A has been formed and acorresponding crowd record has been created and stored. Between TIME 1and TIME 2, three users from CROWD A have moved, thereby resulting inthe removal of those three users from CROWD A as well as the removal ofCROWD A. Again, CROWD A has been removed by removing the set or list ofuser records and spatial information from the crowd record for CROWD A.At TIME 2, a new crowd, CROWD B, has been formed for the three usersthat were previously in CROWD A. As such, the previous crowd fields forthe three users now in CROWD B indicate that the three users are fromCROWD A. Using the process of FIG. 58, the crowd analyzer 58 determinesthat the three users in CROWD B have a common previous crowd, namely,CROWD A. As a result, the crowd analyzer 58 re-establishes CROWD B asCROWD A, as shown at TIME 2′.

FIG. 60 graphically illustrates the process for capturing a crowd splitfor an exemplary crowd according to one embodiment of the presentdisclosure. As illustrated, at TIME 1, CROWD A has been formed and acorresponding crowd record has been created and stored. Between TIME 1and TIME 2, four users from CROWD A have separated from the other threeusers of CROWD A. As a result, a new crowd, CROWD B, has been formed atTIME 2 for the four users from CROWD A. Using the process of FIG. 58,the crowd analyzer 58 determines that the four users in CROWD B are allfrom CROWD A and therefore identifies CROWD B as being split from CROWDA.

FIG. 61 graphically illustrates the merging of two exemplarypre-existing crowds according to one embodiment of the presentdisclosure. As discussed above, the merger of crowds is performed duringthe spatial crowd formation process of FIGS. 56A through 56D. Asillustrated, at TIME 1, CROWD A and CROWD B have been formed andcorresponding crowd records have been created and stored. Between TIME 1and TIME 2, CROWD A and CROWD B move close to one another such that thedistance between CROWD A and CROWD B is less than the optimal inclusiondistance(s) at TIME 2. As such, the crowd analyzer 58 merges CROWD Ainto CROWD B at TIME 2′. As part of the merger, CROWD A is removed, andthe merged into field of the crowd record for CROWD A is set to areference to the crowd record for CROWD B. In addition, the previouscrowd fields in the user records of the user from CROWD A are set to areference to the crowd record of CROWD A.

FIG. 62 illustrates the operation of the MAP server 12 of FIG. 1 toserve a request for crowd tracking data for a crowd according to oneembodiment of the present disclosure. First, the subscriber device 22sends a crowd tracking data request for a crowd to the MAP server 12(step 4100). Note that access to crowd tracking data is preferably asubscription service only available to subscribers, such as thesubscriber 24 at the subscriber device 22, for a subscription fee. Thecrowd tracking data request identifies a particular crowd. For example,in one embodiment, the crowd data for a number of crowds near a POI orwithin an AOI is presented to the subscriber 24 at the subscriber device22 in the manner described above. The subscriber 24 may then select oneof those crowds and initiate a request for crowd tracking data for theselected crowd. In response, the subscriber device 22 sends the crowdtracking data request for the selected crowd to the MAP server 12.

In response to receiving the crowd tracking data request, the MAP server12, and more specifically the crowd analyzer 58, obtains relevant crowdsnapshots for the crowd (step 4102). In one embodiment, the crowdtracking data request is a general crowd tracking data request for thecrowd. As such, the relevant crowd snapshots are all crowd snapshots forthe crowd. In another embodiment, the crowd tracking data request mayinclude one or more criteria to be used to identify the relevant crowdsnapshots. The one or more criteria may include time-based criteria suchthat only those crowd snapshots for the crowd that satisfy thetime-based criteria are identified as the relevant crowd snapshots. Forexample, the time-based criteria may define a range of dates such asOct. 1, 2009 through Oct. 8, 2009 or define a range of times within aparticular day such as 5 pm through 9 pm on Oct. 1, 2009. The one ormore criteria may additionally or alternatively include user-basedcriteria such that only those crowd snapshots including anonymous userssatisfying the user-based criteria are identified as the relevant crowdsnapshots. For example, the user-based criteria may include one or moreinterests and a minimum number or percentage of users such that onlythose crowd snapshots including at least the minimum number orpercentage of anonymous users having the one or more interests areidentified as the relevant crowd snapshots. Note that by usinguser-based criteria, the subscriber 24 is enabled to track sub-crowdswithin a crowd.

Next, the crowd analyzer 58 of the MAP server 12 generates crowdtracking data for the crowd based on the relevant crowd snapshots (step4104). The crowd tracking data includes data indicative of the locationof the crowd over time, which can be determined based on the spatialinformation and sample times from the relevant crowd snapshots. Inaddition, the crowd tracking data may include an aggregate profile forthe crowd for each of the relevant crowd snapshots or at least some ofthe relevant crowd snapshots, an average aggregate profile for all ofthe relevant crowd snapshots, an average aggregate profile for a subsetof the relevant crowd snapshots, or average aggregate profiles for anumber of subsets of the relevant crowd snapshots. For example, therelevant crowd snapshots may be divided into a number of time bands suchthat at least some of the time bands include multiple relevant crowdsnapshots. An average crowd snapshot may then be created for each of thetime bands. The crowd analyzer 58 may utilize the aggregation engine 60to obtain an aggregate profile for a crowd snapshot based on theinterests of the anonymous users in the crowd snapshot. Morespecifically, in a manner similar to that described above, an aggregateprofile for a crowd snapshot may be computed by comparing the interestsof the anonymous users to one another or by comparing the interests ofthe anonymous users to a target profile. The crowd tracking data mayalso contain other information derived from the relevant crowd snapshotssuch as, for example, the number of users in the relevant crowdsnapshots, crowd characteristics for the crowd for the relevant crowdsnapshots, or the like.

The crowd analyzer 58 returns the crowd tracking data for the crowd tothe subscriber device 22 (step 4106). Note that in the embodiment wherethe subscriber device 22 interacts with the MAP server 12 via the webbrowser 38, the MAP server 12 returns the crowd tracking data to thesubscriber device 22 in a format suitable for use by the web browser 38.For example, the crowd tracking data may be returned via a web pageincluding a map, wherein indicators of the location of the crowd overtime as defined by the relevant crowd snapshots may be overlaid upon themap. The subscriber 24 may then be enabled to select one of thoseindicators to view additional information regarding the crowd at thattime such as, for example, an aggregate profile of a corresponding crowdsnapshot of the crowd. Once the crowd tracking data is received at thesubscriber device 22, the crowd tracking data is presented to thesubscriber 24 (step 4108).

In addition to enabling an entity, such as the subscriber 24, to trackcrowds, the crowd snapshots of crowds may also be used to provideadditional metrics about the crowds. These metrics may be included inthe crowd data generated for the crowds and returned to the users 20-1through 20-N, the subscriber 24, or the third-party service 26. Forexample, a quality factor for a crowd may be provided as a function of aduration of time that the crowd has existed. The duration of time thatthe crowd has existed can be determined from the crowd snapshots for thecrowd. For instance, a crowd may have a high quality if the crowd hasexisted (not been removed) for a duration of two or more hours, a lowquality if the crowd has existed for a duration of less than fiveminutes, or one of various intermediate degrees of quality if the crowdhas existed for a duration of between five minutes and two hours. Notethat when determining whether to remove a user from a crowd, the qualityof the crowd may be used to relax or stretch the optimal inclusiondistance for the crowd with respect to user removal. This relaxation orstretching of the optimal inclusion distance with respect to userremoval may then retract to its original value after or over a desiredperiod of time. The retracting period may also be a function of thequality of the crowd. In this manner, if a crowd has existed for a longperiod of time, the MAP server 12 will be more lenient when determiningwhether to remove a user from that crowd because the crowd is stable andthe user will likely move back to within the optimal inclusion distancefrom the center of the crowd.

As another example, the crowd snapshots of a crowd may be used tocompute a motility of the crowd based on how much area the crowd coversover time. For instance, the distance that the crowd has traveled over aperiod of time may be determined based on the crowd centers stored inthe crowd snapshots for the crowd during that period of time. The totaldistance traveled over the period of time can be provided as themotility of the crowd. The motility of the crowd may additionally oralternatively consider a speed at which the crowd moves over a period oftime.

FIG. 63 illustrates the operation of the MAP server 12 to enable alertsaccording to one embodiment of the present disclosure. In thisembodiment, the MAP client 30-1 of the mobile device 18-1 interacts withthe MAP server 12 via the MAP client 30-1 to configure an alert (steps4200 and 4202). More specifically, in this embodiment, the user 20-1interacts with the MAP application 32-1 to provide input defining adesired alert. In one embodiment, the alert may be configured such thatthe user 20-1 is alerted when a particular crowd meets one or morecriteria specified by the user 20-1 or when one or more crowds relevantto a POI or an AOI meet one or more criteria specified by the user 20-1.The one or more criteria may be based on the aggregate profile of therelevant crowd(s), the characteristics of the relevant crowd(s), thequality of the relevant crowd(s), or a combination thereof. For example,the one or more criteria specified for the alert may include anaggregate profile based criterion such that the user 20-1 is alertedwhen the aggregate profile of the crowd specified by the user 20-1satisfies the aggregate profile based criterion or when the aggregateprofile of one or more crowds relevant to the POI or the AOI specifiedby the user 20-1 satisfies the aggregate profile based criterion. Theaggregate profile based criterion may be that a minimum match strengthfor the aggregate profile of the crowd as compared to the user profileof the user 20-1. As another example, the one or more criteria for thealert may include one or more user-based criteria such as, for example,a criterion stating that the user 20-1 is to be alerted when a definedminimum number of users in the crowd have a specified interest. As yetanother example, the one or more criterion for the alert may include acriterion stating that the user 20-1 is to be alerted when a crowdhaving at least a specified number of users is at or near a specifiedPOI or AOI or when a specified crowd has at least a specified number ofusers.

Once the alert is configured, the MAP server 12 monitors the crowdspecified for the alert or crowds relevant to the POI or the AOIspecified for the alert to detect when the one or more criteria for thealert are satisfied (step 4204). Once the one or more criteria for thealert are satisfied, the alert is triggered such that the MAP server 12sends the alert to the MAP client 30-1, which in turn sends the alert tothe MAP application 32-1 (steps 4206 and 4208). The MAP application 32-1then presents the alert to the user 20-1 (step 4210). The alert may bepresented as, for example, a visual alert or an audible alert.

FIG. 64 is a block diagram of the MAP server 12 according to oneembodiment of the present disclosure. As illustrated, the MAP server 12includes a controller 350 connected to memory 352, one or more secondarystorage devices 354, and a communication interface 356 by a bus 358 orsimilar mechanism. The controller 350 is a microprocessor, digitalApplication Specific Integrated Circuit (ASIC), Field Programmable GateArray (FPGA), or the like. In this embodiment, the controller 350 is amicroprocessor, and the application layer 40, the business logic layer42, and the object mapping layer 62 (FIG. 2) are implemented in softwareand stored in the memory 352 for execution by the controller 350.Further, the datastore 64 (FIG. 2) may be implemented in the one or moresecondary storage devices 354. The secondary storage devices 354 aredigital data storage devices such as, for example, one or more hard diskdrives. The communication interface 356 is a wired or wirelesscommunication interface that communicatively couples the MAP server 12to the network 28 (FIG. 1). For example, the communication interface 356may be an Ethernet interface, local wireless interface such as awireless interface operating according to one of the suite of IEEE802.11 standards, or the like.

FIG. 65 is a block diagram of the mobile device 18-1 according to oneembodiment of the present disclosure. This discussion is equallyapplicable to the other mobile devices 18-2 through 18-N. Asillustrated, the mobile device 18-1 includes a controller 360 connectedto memory 362, a communication interface 364, one or more user interfacecomponents 366, and the location function 36-1 by a bus 368 or similarmechanism. The controller 360 is a microprocessor, digital ASIC, FPGA,or the like. In this embodiment, the controller 360 is a microprocessor,and the MAP client 30-1, the MAP application 32-1, and the third-partyapplications 34-1 are implemented in software and stored in the memory362 for execution by the controller 360. In this embodiment, thelocation function 36-1 is a hardware component such as, for example, aGPS receiver. The communication interface 364 is a wirelesscommunication interface that communicatively couples the mobile device18-1 to the network 28 (FIG. 1). For example, the communicationinterface 364 may be a local wireless interface such as a wirelessinterface operating according to one of the suite of IEEE 802.11standards, a mobile communications interface such as a cellulartelecommunications interface, or the like. The one or more userinterface components 366 include, for example, a touchscreen, a display,one or more user input components (e.g., a keypad), a speaker, or thelike, or any combination thereof.

FIG. 66 is a block diagram of the subscriber device 22 according to oneembodiment of the present disclosure. As illustrated, the subscriberdevice 22 includes a controller 370 connected to memory 372, one or moresecondary storage devices 374, a communication interface 376, and one ormore user interface components 378 by a bus 380 or similar mechanism.The controller 370 is a microprocessor, digital ASIC, FPGA, or the like.In this embodiment, the controller 370 is a microprocessor, and the webbrowser 38 (FIG. 1) is implemented in software and stored in the memory372 for execution by the controller 370. The one or more secondarystorage devices 374 are digital storage devices such as, for example,one or more hard disk drives. The communication interface 376 is a wiredor wireless communication interface that communicatively couples thesubscriber device 22 to the network 28 (FIG. 1). For example, thecommunication interface 376 may be an Ethernet interface, local wirelessinterface such as a wireless interface operating according to one of thesuite of IEEE 802.11 standards, a mobile communications interface suchas a cellular telecommunications interface, or the like. The one or moreuser interface components 378 include, for example, a touchscreen, adisplay, one or more user input components (e.g., a keypad), a speaker,or the like, or any combination thereof.

FIG. 67 is a block diagram of a computing device 382 operating to hostthe third-party service 26 according to one embodiment of the presentdisclosure. The computing device 382 may be, for example, a physicalserver. As illustrated, the computing device 382 includes a controller384 connected to memory 386, one or more secondary storage devices 388,a communication interface 390, and one or more user interface components392 by a bus 394 or similar mechanism. The controller 384 is amicroprocessor, digital ASIC, FPGA, or the like. In this embodiment, thecontroller 384 is a microprocessor, and the third-party service 26 isimplemented in software and stored in the memory 386 for execution bythe controller 384. The one or more secondary storage devices 388 aredigital storage devices such as, for example, one or more hard diskdrives. The communication interface 390 is a wired or wirelesscommunication interface that communicatively couples the computingdevice 382 to the network 28 (FIG. 1). For example, the communicationinterface 390 may be an Ethernet interface, local wireless interfacesuch as a wireless interface operating according to one of the suite ofIEEE 802.11 standards, a mobile communications interface such as acellular telecommunications interface, or the like. The one or more userinterface components 392 include, for example, a touchscreen, a display,one or more user input components (e.g., a keypad), a speaker, or thelike, or any combination thereof.

Those skilled in the art will recognize improvements and modificationsto the embodiments of the present invention. All such improvements andmodifications are considered within the scope of the concepts disclosedherein and the claims that follow.

What is claimed is:
 1. A method of operation of a server computer,comprising: obtaining, by the server computer, current locations of aplurality of users of a plurality of mobile devices each of theplurality of users being a user of a corresponding one of the pluralityof mobile devices; forming, by the server computer, a crowd including anumber of users from the plurality of users based on the currentlocations of the number of users, wherein forming the crowd comprisesusing a spatial crowd formation process based on an optimal inclusiondistance that is a function of density of users of the plurality ofusers within a bounding region for the spatial crowd formation process,the optimal inclusion distance subject to change as the crowd is formed;generating, by the server computer, crowd data regarding the crowd, thecrowd data comprising an aggregate profile for the crowd; and providing,by the server computer, access to the crowd data in response toreceiving a request for the crowd data.
 2. The method of claim 1 whereingenerating the crowd data regarding the crowd comprises generating theaggregate profile for the crowd based on a comparison of user profilesof the number of users in the crowd and a user profile associated with arequestor for which the aggregate profile for the crowd is generated. 3.The method of claim 2 wherein the user profiles of the number of usersin the crowd and the user profile associated with the requestor eachcomprises one or more keywords, and generating the aggregate profile forthe crowd based on the comparison of the user profiles of the number ofusers in the crowd and the user profile associated with the requestorcomprises: for each user of the number of users in the crowd, performinga comparison of the one or more keywords in the user profile of the userin the crowd to the one or more keywords in the user profile associatedwith the requestor to identify matching keywords; and based on thecomparisons, generating the aggregate profile such that the aggregateprofile includes at least one of a group consisting of: a number of usermatches over all keywords which identify a number of the number of usershaving user profiles that include at least one keyword that matches akeyword in the user profile associated with the requestor, a number ofuser matches for each of one or more keywords from the user profileassociated with the requestor, a ratio of the number of user matchesover all keywords to a total number of users in the crowd, and a ratioof user matches to the total number of users for each of one or morekeywords from the user profile associated with the requestor.
 4. Themethod of claim 2 wherein the requestor is one of the plurality ofusers, and the user profile associated with the requestor is one of agroup consisting of: a user profile of the one of the plurality ofusers, a select subset of a user profile of the one of the plurality ofusers, and a target user profile.
 5. The method of claim 1 whereingenerating the crowd data regarding the crowd comprises generating theaggregate profile for the crowd based on a comparison of user profilesof the number of users in the crowd to one another.
 6. The method ofclaim 5 wherein the user profiles of the number of users in the crowdeach comprise one or more keywords, and generating the aggregate profilefor the crowd based on the comparison of the user profiles of the numberof users in the crowd to one another comprises generating the aggregateprofile such that the aggregate profile includes at least one of a groupconsisting of: a number of user matches for each of one or more keywordsfrom the user profiles of the number of users in the crowd and a ratioof user matches to a total number of users for each of one or morekeywords from the user profiles of the number of users in the crowd. 7.The method of claim 1 wherein generating the crowd data regarding thecrowd comprises generating data identifying a degree of fragmentation ofthe crowd such that the crowd data comprises the data identifying thedegree of fragmentation of the crowd.
 8. The method of claim 7 whereinthe data identifying the degree of fragmentation of the crowd comprisesat least one of a group consisting of: a number of crowd fragments inthe crowd and an average number of users per crowd fragment in thecrowd.
 9. The method of claim 7 wherein generating the data identifyingthe degree of fragmentation of the crowd comprises dividing the crowdinto one or more crowd fragments using a spatial crowd fragmentationprocess.
 10. The method of claim 9 wherein dividing the crowd into theone or more crowd fragments using the spatial crowd fragmentationprocess comprises: creating a crowd fragment for each user in the crowd;identifying two closest crowd fragments; determining a distance betweenthe two closest crowd fragments; determining whether the distancebetween the two closest crowd fragments is less than the optimalinclusion distance for a crowd fragment; combining the two closest crowdfragments if the distance between the two closest crowd fragments isless than the optimal inclusion distance for a crowd fragment; andrepeating the steps of identifying two closest crowd fragments,determining a distance between the two closest crowd fragments,determining whether the distance between the two closest crowd fragmentsis less than the optimal inclusion distance for a crowd fragment, andcombining the two closest crowd fragments.
 11. The method of claim 7wherein generating the data identifying the degree of fragmentation ofthe crowd comprises dividing the crowd into one or more crowd fragmentsusing a connectivity-based crowd fragmentation process.
 12. The methodof claim 11 wherein dividing the crowd into the one or more crowdfragments using the connectivity-based crowd fragmentation processcomprises: creating a crowd fragment for each user in the crowd;identifying a pair of crowd fragments having a pair of users including afirst user from a first crowd fragment of the pair of crowd fragmentshaving a required social network relationship with a second user from asecond crowd fragment of the pair of crowd fragments; combining the pairof crowd fragments; and repeating the steps of identifying a pair ofcrowd fragments and combining the pair of crowd fragments.
 13. Themethod of claim 12 wherein the required social network relationship is adegree of separation (DOS) that is less than a predefined maximum DOSand a required bidirectionality state.
 14. The method of claim 1 whereingenerating the crowd data regarding the crowd comprises: dividing thecrowd into one or more crowd fragments; and for each crowd fragment ofat least one of the one or more crowd fragments, generating a best-caseaverage degree of separation (DOS) for the crowd fragment such that thecrowd data comprises the best-case average DOS for the crowd fragment.15. The method of claim 14 wherein generating the best-case average DOSfor the crowd fragment comprises generating the best-case average DOSfor the crowd fragment as an average DOS for social networkrelationships between pairs of users in the crowd fragment using abest-case DOS for each pair of users in the crowd fragment for which asocial network relationship is not explicitly defined.
 16. The method ofclaim 1 wherein the crowd data regarding the crowd comprises: dividingthe crowd into one or more crowd fragments; and for each crowd fragmentof at least one of the one or more crowd fragments, generating aworst-case average degree of separation (DOS) for the crowd fragmentsuch that the crowd data comprises the worst-case average DOS for thecrowd fragment.
 17. The method of claim 16 wherein generating theworst-case average DOS for the crowd fragment comprises generating theworst-case average DOS for the crowd fragment as an average DOS forsocial network relationships between pairs of users in the crowdfragment using a worst-case DOS for each pair of users in the crowdfragment for which a social network relationship is not explicitlydefined.
 18. The method of claim 1 wherein the crowd data regarding thecrowd comprises: dividing the crowd into one or more crowd fragments;and for each crowd fragment of at least one of the one or more crowdfragments, generating data identifying a degree of bidirectionality offriend relationships between users in the crowd fragment such that thecrowd data comprises the data identifying the degree of bidirectionalityof friend relationships between the users in the crowd fragment.
 19. Themethod of claim 18 wherein generating the data identifying the degree ofbidirectionality of friend relationships between the users in the crowdfragment comprises computing a ratio of bidirectional friendrelationships between pairs of users in the crowd fragment to a totalnumber of friend relationships between pairs of users in the crowdfragment.
 20. A server computer comprising: a controller; and memorycontaining software executable by the controller, whereby the servercomputer is configured to: obtain current locations of a plurality ofusers of a plurality of mobile devices, each of the plurality of usersbeing a user of a corresponding one of the plurality of mobile devices;form a crowd including a number of users from the plurality of usersbased on the current locations of the number of users, wherein the crowdis formed using a spatial crowd formation process based on an optimalinclusion distance that is a function of density of users of theplurality of users within a bounding region for the spatial crowdformation process, the optimal inclusion distance subject to change asthe crowd is formed; generate crowd data regarding the crowd, the crowddata comprising an aggregate profile for the crowd; and provide accessto the crowd data in response to receiving a request for the crowd data.21. A non-transitory computer readable medium storing software forinstructing a controller of a computing device to: obtain currentlocations of a plurality of users of a plurality of mobile devices, eachof the plurality of users being a user of a corresponding one of theplurality of mobile devices; form a crowd including a number of usersfrom the plurality of users based on the current locations of the numberof users, wherein to form the crowd, the software includes software forinstructing the controller to form the crowd using a spatial crowdformation process based on an optimal inclusion distance that is afunction of density of users of the plurality of users within a boundingregion for the spatial crowd formation process, the optimal inclusiondistance subject to change as the crowd is formed; generate crowd dataregarding the crowd, the crowd data comprising an aggregate profile forthe crowd; and provide access to the crowd data in response to receivinga request for the crowd data.